Search

Although in-feed advertising is popular on mainstream platforms, academic research on it is limited. Platforms typically deliver organic content through two methods: subscription by users or recommendation by artificial intelligence. However, little is known about the ad performance between these two channels. This research examines how the performance of in-feed ads, in terms of click-through rates and conversion rates, differs between subscription and recommendation channels and whether these effects are mediated by ad intrusiveness and moderated by ad attributes. Two ad attributes are investigated: ad appeal (informational vs. emotional) and ad link (direct vs. indirect). Study 1 finds that the recommendation channel generates higher click-through rates but lower conversion rates than the subscription channel, and these effects are amplified by informational ad appeal and direct ad links. Study 2 explores channel differences, revealing that the recommendation channel yields less source credibility and content control, reducing consumer engagement with organic content. Studies 3 and 4 validate the mediating role of ad intrusiveness and rule out ad recognition as an alternative explanation. Study 5 uses eye-tracking technology to show that the recommendation channel has lower content engagement, lower ad intrusiveness, and greater ad interest. [ABSTRACT FROM AUTHOR]

Recommendation has become especially crucial during the COVID-19 pandemic as a significant number of people rely on online shopping from home. Existing recommendation algorithms, designed to address issues like cold start and data sparsity, often overlook the time constraints of users. Specifically, users expect to receive recommendations for products of interest in the shortest possible time. To address this challenge, we propose a novel collaborative filtering recommendation algorithm that leverages the advantages of quantum computing circuits based on data reconstruction. This approach allows for the rapid identification of users similar to the target user, thereby improving recommendation speed. In our method, we utilize the information of known users to linearly reconstruct that of the target users, forming a relational matrix. Subsequently, we employ \(l_{2,1}-\) norm and \(l_{1}-\) norm to sparsely constrain the relationship matrix, deducing the weight of each known user. The final step involves providing similar recommendations to target users based on these weights. Furthermore, we implement the proposed algorithm using a quantum circuit, enabling exponential acceleration. The final weight matrix is derived from the quantum state outputted by the circuit. The speed of this process is theoretically demonstrated in detail. Experimental results indicate that our algorithm outperforms state-of-the-art methods in terms of root mean squared error (RMSE), mean absolute error (MAE) and normalized discounted cumulative gain (NDCG). Compared to state-of-the-art comparison algorithms, the proposed algorithm achieves the fastest recommendation speed across eight public datasets. [ABSTRACT FROM AUTHOR]

In the swiftly evolving domain of online commerce, the significance of recommendation systems has risen alongside the rapid expansion of internet usage over the past decade. As online commerce continues to thrive, recommendation systems serve a crucial function in steering users towards pertinent products amidst the expansive online environment. Nonetheless, conventional collaborative filtering algorithms often encounter challenges such as sparse data and shifting user preferences, necessitating innovative approaches. Our proposed recommendation system aims to tackle these hurdles by seamlessly blending collaborative filtering and content-based filtering methodologies. It will offer product suggestions for both new and existing users. Through thorough examination of users' past purchasing behaviors, patterns, and feedback, our system customizes recommendations precisely to cater to existing users' needs. The initial stage involves feature extraction, wherein both content-based and collaborative features are obtained by creating user profiles, computing content similarity, identifying related items, generating recommendations, and suggesting items. Additionally, new users will receive recommendations for novel and trending products. Leveraging the Walmart Product rating Dataset, our system continuously enhances recommendations based on evolving user interactions, thus optimizing engagement and satisfaction levels. This study underscores the pivotal role of advanced recommendation techniques in transforming the online commerce landscape, ensuring informed purchasing decisions, heightened user satisfaction, and increased sales. [ABSTRACT FROM AUTHOR]

Graph convolutional neural networks have been successfully applied to collaborative filtering to capture high-quality user-item representations. Despite their remarkable performance, there are still limitations that hinder further improvement of recommender systems. Most existing recommendation systems utilize implicit feedback data for model training, but such data inevitably contains adversarial interaction noise. The conventional graph-based collaborative filtering method fails to effectively filter out this noise, and instead amplifies its impact, resulting in degraded model performance. To address this issue, we propose a robustness-enhanced collaborative filtering graph neural network model that does not rely on explicit noise filtering. Our approach involves simulating user-item interactions that do not exist in practice as adversarial interaction noise using random noise. To mitigate the impact of this noise in hidden feedback, we replace them with randomly selected partial nodes based on the principle of mutual information maximization. Our model has been extensively experimented on three public datasets (MovieLens-1 M, Yelp, and Ta-feng) and achieves performance improvements of about 5%, 10%, and 14%, respectively, compared to the state-of-the-art baseline model. In particular, in model robustness experiments, our model achieves significant performance improvements of about 13% and 17% in Yelp and Ta-feng. A comprehensive experimental study shows that our proposed method is reasonably effective and interpretable. [ABSTRACT FROM AUTHOR]

Most existing collaborative filtering-based recommender systems rely solely on available user–item interactions for user and item representation learning. Their performance often suffers significantly when interactions are sparse, as limited user and item interactions are insufficient for learning robust representations. To address this issue, recent research has explored additional information between users and items by leveraging the user–item bipartite graph. However, these methods have not fully exploited high-order neighborhood information, primarily using sampled interactions to enrich training data rather than integrating this information directly into representation learning. In this paper, we propose a novel model, EIR-GCN (Embedding Integration with Relational Graph Convolutional Network), which directly incorporates various types of collaborative relations, such as user–user and item–item interactions, into the embedding function for user preference modeling. Specifically, our model employs advanced graph convolutional network (GCN) techniques to integrate user–item, user–user, and item–item relations for comprehensive representation learning. EIR-GCN initially selects the most influential second-order neighbors from the user–item bipartite graph to form user–user and item–item connections. With these enriched connections, a message-passing method is adopted to learn node representations by aggregating messages from directly linked nodes, including first-order item neighbors and selected second-order user neighbors. Extensive experiments on several public datasets demonstrate that EIR-GCN outperforms strong baselines, including recent GCN-based models and those exploiting high-order information. Our results show that EIR-GCN achieves state-of-the-art performance and effectively addresses the sparsity issue, highlighting its robustness and efficacy in recommendation tasks. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing paradigm and mobile devices, the number of cloud services and user interest data are growing explosively. It becomes harder for users to find suitable cloud services within satisfying response time. Thus a suitable cloud service automatic recommendation system is needed to solve the above problem. In this work, we propose KG2Rec, a novel recommendation method based on Knowledge Graph to provide users with appropriate recommendations to meet their real-time needs. The property-specific knowledge graph model is designed to model user–item and item–item bipartite relations. Based on the property-specific knowledge graph model, property-specific user–item relation features can be mined from it. Then the user–item relation features will be integrated into an improved collaborative filtering algorithm based on the local sensitive hash technique for Top-N item recommendation. Finally, we evaluate the proposed method in terms of Top-N recommendation on the MovieLens dataset, and prove it outperforms numbers of state-of-the-art recommendation systems. In addition, we prove it has well performance in term of long tail recommendation and big data processing. [ABSTRACT FROM AUTHOR]

Multi-scenario and multi-task recommendation can use various feedback behaviors of users in different scenarios to learn users' preferences and then make recommendations, which has attracted attention. However, the existing work ignores feature interactions and the fact that a pair of feature interactions will have differing levels of importance under different scenario-task pairs, leading to sub-optimal user preference learning. In this article, we propose a Multi-scenario and Multi-task aware Feature Interaction model, dubbed MMFI, to explicitly model feature interactions and learn the importance of feature interaction pairs in different scenarios and tasks. Specifically, MMFI first incorporates a pairwise feature interaction unit and a scenario-task interaction unit to effectively capture the interaction of feature pairs and scenario-task pairs. Then MMFI designs a scenario-task aware attention layer for learning the importance of feature interactions from coarse-grained to fine-grained, improving the model's performance on various scenario-task pairs. More specifically, this attention layer consists of three modules: a fully shared bottom module, a partially shared middle module, and a specific output module. Finally, MMFI adapts two sparsity-aware functions to remove some useless feature interactions. Extensive experiments on two public datasets demonstrate the superiority of the proposed method over the existing multi-task recommendation, multi-scenario recommendation, and multi-scenario & multi-task recommendation models. [ABSTRACT FROM AUTHOR]

The topic of privacy-preserving collaborative filtering is gaining more and more attention. Nevertheless, privacy-preserving collaborative filtering techniques are vulnerable to shilling or profile injection assaults. Hence, it is crucial to identify counterfeit profiles in order to achieve total success. Various techniques have been devised to identify and prevent intrusion patterns from infiltrating the system. Nevertheless, these strategies are specifically designed for collaborative filtering algorithms that do not prioritize privacy. There is a scarcity of research on identifying shilling attacks in recommender systems that prioritize privacy. This work presents a novel technique for identifying shilling assaults in privacy-preserving collaborative filtering systems. We employ an ant colony clustering detection method to effectively identify and eliminate fake profiles that are created by six widely recognized shilling attacks on compromised data. The objective of the study is to categorize the fraudulent profiles into a specific cluster and separate this cluster from the system. Empirical experiments are conducted with actual data. The empirical findings demonstrate that the strategy derived from the study effectively eliminates fraudulent profiles in privacy-preserving collaborative filtering. [ABSTRACT FROM AUTHOR]

Recently, mashup developers seek to integrate multiple services with complementary functionalities from a large amount of web services. With so many available web services, it is difficult for developers to choose the right one to develop new mashups. Therefore, it is critical to create and recommend appropriate web services for mashup developers based on their development needs. In the past, various deep models have been proposed to facilitate web service recommendation based on semantic matching of textual descriptions. However, existing deep approaches mainly match global semantic representations while ignoring descriptive structure and tag information. In this paper, we propose a multi-representation web service recommendation model, which simultaneously extracts global, local and tag representations of the description and tag information, respectively. Moreover, we propose a tag-driven attention mechanism to guide the process of information extraction. Experiments over a real-world dataset demonstrate that our proposed service recommendation algorithm can achieve remarkable performance. [ABSTRACT FROM AUTHOR]

Model-based systems engineering (MBSE) is a modeling approach used in industry to support the formalization, analysis, design, checking and verification of systems. In MBSE modeling, domain knowledge is the basis of the modeling. However, modeling does not happen overnight; it requires systematic training and a significant investment of resources. Unfortunately, many domain experts lack the expertise required for modeling, even though they know the domain well. The question arises about how to provide system modelers with domain knowledge at the right time to support the efficient completion of modeling. Since MBSE research that integrates AI is just beginning to take off, no public dataset is available. In this paper, aerospace SysML models are constructed based on spacecraft-related domain knowledge to form SysML model data. The validation rules are studied to validate the SysML model data, and combined with the concept of the recommended system, a recommendation method for the MBSE modeling process based on the knowledge and SysML model is proposed. A GLOVE language model is pre-trained by using domain knowledge and general knowledge; the model data are also used to fine-tune the GLOVE language model combined with the pre-training to recommend some domain development processes. The recommendation list is manually quality-verified and fed into the pre-training phase, while new requirement texts are continuously added in the fine-tuning phase, resulting in a more relevant and accurate recommendation list. Experiments show that the incremental recommender system can not only effectively recommend SysML models, but also improve the quality and efficiency of MBSE development. [ABSTRACT FROM AUTHOR]

Negative sampling is a strategy used to select nodes in unexposed items. Recently, most recommendation algorithms based on knowledge graph have adopted learning-effective sampling strategies. However, these methods regard negative sampling as a static process, assuming that the user's potential preferences remain unchanged, and ignoring the dynamic interaction between the user and the recommendation system. To solve the above limitations, we propose a Dynamic Negative Sampling for Recommendation with feature matching (DNSR) to explore high-quality negative examples. This method consists of two main components: the sampler and the recommender. Firstly, the sampler obtains the embedded representation of nodes through a simplified graph convolutional network, then perform node sampling in the collaborative knowledge graph based on the positive examples that users have interacted with. To improve the sampling process, we developed a multiple feature matching technique that calculates the probability distribution of candidate node sets. This technique allows the sampler to filter out representative negative examples. Secondly, the recommender with reinforcement learning aims to optimize the ranking of positive and negative examples by providing feedback in the form of reward signals. This incentivizes the sampler to generate negative examples with information value. On three real-world datasets, our proposed method performs remarkably better than the state-of-the-art baselines. We further verify DNSR model performance of feature matching through ablation experiments. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user–item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in deep learning have spurred the development of hybrid models, which extend matrix factorization, particularly with autoencoders, to capture nonlinear item relationships. Despite these advancements, many proposed models often neglect dynamic changes in the rating process and overlook user features. This paper introduces IUAutoTimeSVD++, a novel hybrid model that builds upon autoTimeSVD++. By incorporating item–user features into the timeSVD++ framework, the proposed model aims to address the static nature and sparsity issues inherent in existing models. Our model utilizes a contractive autoencoder (CAE) to enhance the capacity to capture a robust and stable representation of user-specific and item-specific features, accommodating temporal variations in user preferences and leveraging item characteristics. Experimental results on two public datasets demonstrate IUAutoTimeSVD++'s superiority over baseline models, affirming its effectiveness in capturing and utilizing user and item features for temporally adaptive recommendations. [ABSTRACT FROM AUTHOR]

In many application domains of recommender systems, e.g., on media streaming sites, one main goal of the provider of the recommendation service is to increase the engagement of users by helping them discover new types of content they like. Standard collaborative filtering algorithms by design often lead to a certain level of discovery. Nonetheless, in certain domains, it may be helpful to more actively promote content to users beyond their past preference profile ("off-profile") and thereby help users explore new content. However, when showing such off-profile content to users in combination with more familiar content, the new content items may be overlooked. In this research, we explore to what extent digital nudging, i.e., subtly directing user choices in a specific direction, can help to raise the attention and interest of users for off-profile content. We conducted a user study ( N = 1064 ) on a real-world social book recommendation app. We find that users who are nudged towards recommended books of their non-preferred genres significantly more often put these off-profile books on their reading lists, thus confirming the effectiveness of digital nudging in this application. However, we also found that digital nudges may negatively impact the users' beliefs and attitudes towards the system and a more limited intention to use the system in the future. As a result, we find that digital nudging in recommendations, while effective in the short run, must be done with due care, keeping an eye on the overall quality perceptions by users and potentially harmful long-term effects. [ABSTRACT FROM AUTHOR]

The rise of graph neural networks has greatly contributed to the development of recommendation systems, and self-supervised learning has emerged as one of the most important approaches to address sparse interaction data. However, existing methods mostly focus on the recommendation's accuracy while neglecting the role of recommended item diversity in enhancing user interest and merchant benefits. The reason for this phenomenon is mainly due to the bias of popular items, which makes the long-tail items (account for a large proportion) be neglected. How to mitigate the bias caused by item popularity has become one of the hot topics in current research. To address the above problems, we propose a Popularity-Debiased Graph Self-Supervised for Recommendation (PDGS). Specifically, we apply a penalty constraint on item popularity during the data enhancement process on the user–item interaction graph to eliminate the inherent popularity bias. We generate item similarity graphs with the popularity bias removed to construct a self-supervised learning task under multiple views, and we design model optimization strategies from the perspectives of popular items and long-tail items to generate recommendation lists. We conduct a large number of comparison experiments, as well as ablation experiments, on three public datasets to verify the effectiveness and the superiority of the model in balancing recommendation accuracy and diversity. [ABSTRACT FROM AUTHOR]

User–item interactions on e-commerce platforms involve various intents, such as browsing and purchasing, which require fine-grained intent recognition. Existing recommendation methods incorporate latent intent into user–item interactions; however, they overlook important considerations. First, they fail to integrate intents with semantic information in knowledge graphs, neglecting intent interpretability. Second, they do not exploit the structural information from multiple views of latent intents in user–item interactions. This study established the intent with knowledge-aware multiview contrastive learning (IKMCL) model for explanation in recommendation systems. The proposed IKMCL model converts latent intent into fine-grained intent, calculates intent weights, mines latent semantic information, and learns the representation of user–item interactions through multiview intent contrastive learning. In particular, we combined fine-grained intents with a knowledge graph to calculate intent weights and capture intent semantics. The IKMCL model performs multiview intent contrastive learning at both coarse-grained and fine-grained levels to extract semantic relationships in user–item interactions and provide intent recommendations in structural and semantic views. In addition, an intent-relational path was designed based on multiview contrastive learning, enabling the capture of semantic information from latent intents and personalized item recommendations with interpretability. Experimental results using large benchmark datasets indicated that the proposed model outperformed other advanced methods, significantly improving recommendation performance. [ABSTRACT FROM AUTHOR]

This paper proposes a method to generate a synthetic rating matrix based on user's rational behavior, with the aim of generating a large-scale rating matrix at low cost. Collaborative filtering is one of the major techniques for recommender systems, which is widely used because it can recommend items using only a history of ratings given to the items by users. However, collaborative filtering has some problems such as the cold-start problem and the sparsity problem, both of which are caused by the shortage of ratings in a database (rating matrix). This problem is particularly serious for services that have just started operation or do not have a large number of users. The proposed method generates a rating matrix without missing values using users' rating probabilities, which are obtained from the distribution of their actual ratings. The final synthetic rating matrix is generated after adjusting its sparsity by introducing missing values. The validity of the proposed method is evaluated by comparing the synthetic rating matrix in terms of the similarity of the distribution of several statistics with that of the real data. The synthetic rating matrix is also evaluated by applying it to recommendation to actual users. The experimental results show that the proposed method can generate the synthetic rating matrix that has similar statistics to the real data, and recommendation models trained with the synthetic data achieve comparable recall to that trained with the real data when using the real data as test data. Based on the results of these experiments, this paper also tries to generate the synthetic rating matrix that contains richer information than the real data by increasing the number of users or reducing the sparsity of the rating matrix. The results of these experiments show the possibility that increasing the information contained in a rating matrix could improve recall. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel personalized music recommendation system that leverages collaborative filtering and semantic analysis techniques. The system aims to generate customized music recommendations tailored to individual users' preferences and listening histories. By employing collaborative filtering, the system identifies users with similar music tastes and suggests music they enjoy. Additionally, semantic analysis is utilized to analyze the current sentiment of a user, enabling the system to recommend songs suitable for the user's mood for initial recommendations. A dataset containing user listening histories and music metadata is utilized to evaluate the system's performance. The results demonstrate that the proposed system surpasses traditional collaborative filtering approaches in identifying the coldstart issue for new users. This capability enhances the music listening experience by providing more relevant and personalized recommendations while facilitating the discovery of new and captivating music. The system's unique approach of combining collaborative filtering and semantic analysis provides a fresh perspective on personalized music recommendations, contributing to the advancement of music recommendation systems. The evaluation outcomes signify the promising nature of the proposed method. Future research can focus on further optimizing the system's performance through additional refinements and evaluations. [ABSTRACT FROM AUTHOR]

General video search and recommendation systems primarily rely on metadata and personal information. Metadata includes file names, keywords, tags, and genres, among others, and is used to describe the video's content. The video platform assesses the relevance of user search queries to the video metadata and presents search results in order of highest relevance. Recommendations are based on videos with metadata judged to be similar to the one the user is currently watching. Most platforms offer search and recommendation services by employing separate algorithms for metadata and personal information. Therefore, metadata plays a vital role in video search. Video service platforms develop various algorithms to provide users with more accurate search results and recommendations. Quantifying video similarity is essential to enhance the accuracy of search results and recommendations. Since content producers primarily provide basic metadata, it can be abused. Additionally, the resemblance between similar video segments may diminish depending on its duration. This paper proposes a metadata expansion model that utilizes object recognition and Speechto-Text (STT) technology. The model selects key objects by analyzing the frequency of their appearance in the video, extracts audio separately, transcribes it into text, and extracts the script. Scripts are quantified by tokenizing them into words using text-mining techniques. By augmenting metadata with key objects and script tokens, various video content search and recommendation platforms are expected to deliver results closer to user search terms and recommend related content. [ABSTRACT FROM AUTHOR]

As online social media platforms continue to proliferate, users are faced with an overwhelming amount of information, making it challenging to filter and locate relevant information. While personalized recommendation algorithms have been developed to help, most existing models primarily rely on user behavior observations such as viewing history, often overlooking the intricate connection between the reading content and the user's prior knowledge and interest. This disconnect can consequently lead to a paucity of diverse and personalized recommendations. In this paper, we propose a novel approach to tackle the multifaceted issue of recommendation. We introduce the Dual-Observation-based approach for the Recommendation (DOR) system, a novel model leveraging dual observation mechanisms integrated into a deep neural network. Our approach is designed to identify both the core theme of an article and the user's unique engagement with the article, considering the user's belief network, i.e., a reflection of their personal interests and biases. Extensive experiments have been conducted using real-world datasets, in which the DOR model was compared against a number of state-of-the-art baselines. The experimental results explicitly demonstrate the reliability and effectiveness of the DOR model, highlighting its superior performance in news recommendation tasks. [ABSTRACT FROM AUTHOR]

Online recommendation systems process large amounts of information to make personalized recommendations. There has been some progress in research on incorporating knowledge graphs in reinforcement learning for recommendation; however, some challenges still remain. First, in these approaches, an agent cannot switch paths intelligently, because of which, the agent cannot cope with multi-entities and multi-relations in knowledge graphs. Second, these methods do not have predefined targets and thus cannot discover items that are closely related to user-interacted items and latent rich semantic relationships. Third, contemporary methods do not consider long rational paths in knowledge graphs. To address these problems, we propose a deep knowledge reinforcement learning (DKRL) framework, in which path-guided intelligent switching was implemented over knowledge graphs incorporating reinforcement learning; this model integrates predefined target and long logic paths over knowledge graphs for recommendation systems. Specifically, the designed novel path-based intelligent switching algorithm with predefined target enables an agent to switch paths intelligently among multi-entities and multi-relations over knowledge graphs. In addition, the weight of each path is calculated, and the agent switches paths between multiple entities according to path weights. Furthermore, the long logic path has better recommendation performance and interpretability. Extensive experiments with actual data demonstrate that our work improves upon existing methods.The experimental results indicated that DKRL improved the baselines of NDCG@10 by 3.7%, 9.3%, and 4.7%; of HR@10 by 12.39%, 20.8%, and 13.86%; of Prec@10 by 5.17%, 3.57%, 6.2%; of Recall@10 by 3.01%, 4.2%, and 3.37%. The DKRL model achieved more effective recommendation performance using several large benchmark data sets compared with other advanced methods. [ABSTRACT FROM AUTHOR]

The precision of artificial intelligence (AI)‐generated information has been suggested in the past as a method of nudging consumers' evaluations and intentions, but little is known about whether such effects are also context‐sensitive. Based on four studies, we find a matched condition under which consumers are more likely to make a positive response when precise (imprecise) numbers presented by AI recommenders are used in a utilitarian (hedonic) consumption context (Study 1). Additionally, we show that consumer conceptual fluency also mediates this matching effect on consumer purchase decision‐making (Studies 2). We further show the matching effect is moderated by the recommender type (Study 3) and consumer lay beliefs about the AI and human recommenders (Study 4). This study shows that when consumers' lay belief changes from "AI performs objective tasks well" and "Human performs subjective tasks well" to "AI performs subjective tasks well" and "Human performs objective tasks well," it can change the difference in the matching relationship between human and AI recommenders. [ABSTRACT FROM AUTHOR]

Copyright of Policy & Internet is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conversation Recommender System (CRS) engage in multi-turn conversations with users and provide recommendations through responses. As user preferences evolve dynamically during the course of the conversation, it is crucial to understand natural interaction utterances to capture the user's dynamic preference accurately. Existing research has focused on obtaining user preference at the entity level and natural language level, and bridging the semantic gap through techniques such as knowledge augmentation, semantic fusion, and prompt learning. However, the representation of each level remains under-explored. At the entity level, user preference is typically extracted from Knowledge Graphs, while other modal data is often overlooked. At the natural language level, user representation is obtained from a fixed language model, disregarding the relationships between different contexts. In this paper, we propose User-augmented Conversation Recommendation via Multi-modal graph learning and Context Mining (UaMC) to address above limitations. At the entity level, we enrich user preference by leveraging multi-modal knowledge. At the natural language level, we employ contrast learning to extract user preference from similar contexts. By incorporating the enhanced representation of user preference, we utilize prompt learning techniques to generate responses related to recommended items. We conduct experiments on two public CRS benchmarks, demonstrating the effectiveness of our approach in both the recommendation and conversation subtasks. [ABSTRACT FROM AUTHOR]

Timely diagnosis is crucial for a patient's future care and treatment. However, inadequate medical service or a global pandemic can limit physical contact between patients and healthcare providers. Combining the available healthcare data and artificial intelligence methods might offer solutions that can support both patients and healthcare providers. This study developed one of the artificial intelligence methods, artificial neural network (ANN), the multilayer perceptron (MLP), for medical specialist recommendation systems. The input of the system is symptoms and comorbidities. Meanwhile, the output is the medical specialist. Leave one out crossvalidation technique was used. As a result, this study's F1 score of the model was about 0.84. In conclusion, the ANN system can be an alternative to the medical specialist recommendation system. [ABSTRACT FROM AUTHOR]

Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users' preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users' opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using 'TripAdvisor reviews', 'TMDB 5000 movies', and an 'Amazon dataset'. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past. [ABSTRACT FROM AUTHOR]

To solve the information overload issue and enhance the user experience of various web applications, recommender systems aim to better model user interests and preferences. Knowledge Graphs (KGs), consisting of real-world objective facts and fruitful entities, play a vital role in recommender systems. Recently, a technological trend has been to develop end-to-end Graph Neural Networks (GNNs)-based knowledge-aware recommendation (a.k.a., Knowledge Graph Recommendation, KGR) models. Unfortunately, current GNNs-based KGR approaches focus on how to capture high-order feature information on KGs while neglecting the following two crucial limitations: 1) The explicitly high-order feature interaction and fusion mechanism and 2) The valid user intent modelling mechanism. As such, these issues lead to insufficient user/item representation learning capability and unsatisfactory KGR performance. In this work, we present a novel Knowledge-enhanced Re commendation with F eature I nteraction and Intent-aware Attention Networks (FIRE) to address the latent intent modelling and high-order feature interaction deficiencies ignored by existing KGR methods. Based on the prototype user/item representation learning leveraging the GNNs-based approach, our model offers the following major improvements: One is the innovative use of Convolutional Neural Networks (CNNs) that perform vertical convolutional (a.k.a., bit-level convolutional) and horizontal convolutional (a.k.a., vector-level convolutional) processes to model multi-granular high-order feature interactions to enhance item-side representation learning. Another is to model users' latent intent factors by utilizing a two-level attention mechanism (i.e., node- and intent-level attention mechanism) to enhance user-side representation learning. Extensive experiments on three KGs domain public datasets demonstrate that our method outperforms the existing state-of-the-art baseline. Last but not least, numerous ablation- and model studies demystify the working mechanism and elucidate the plausibility of the proposed model. [ABSTRACT FROM AUTHOR]

Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without knowledge about a bug's class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning a suitable developer to a new bug by analyzing the history of developers' profiles and analyzing the history of bugs for all developers using machine learning-based recommender systems. Explainable AI (XAI) is AI that humans can understand. It contrasts with "black box" AI, which even its designers cannot explain. By providing appropriate explanations for results, users can better comprehend the underlying insight behind the outcomes, boosting the recommender system's effectiveness, transparency, and confidence. The trained model is utilized in the recommendation stage to calculate relevance scores for developers based on expertise and past bug handling performance, ultimately presenting the developers with the highest scores as recommendations for new bugs. This approach aims to strike a balance between computational efficiency and accurate predictions, enabling efficient bug assignment while considering developer expertise and historical performance. In this paper, we propose two explainable models for recommendation. The first is an explainable recommender model for personalized developers generated from bug history to know what the preferred type of bug is for each developer. The second model is an explainable recommender model based on bugs to identify the most suitable developer for each bug from bug history. [ABSTRACT FROM AUTHOR]

As software systems evolve, they become more complex and larger, creating challenges in predicting change propagation while maintaining system stability and functionality. Existing studies have explored extracting co-change patterns from changelog data using data-driven methods such as dependency networks; however, these approaches suffer from scalability issues and limited focus on high-level abstraction (package level). This article addresses these research gaps by proposing a file-level change propagation to vector (FCP2Vec) approach. FCP2Vec is a recommendation system designed to aid developers by suggesting files that may undergo change propagation subsequently, based on the file being presently worked on. We carried out a case study utilizing three publicly available datasets: Vuze, Spring Framework, and Elasticsearch. These datasets, which consist of open-source Java-based software development changelogs, were extracted from version control systems. Our technique learns the historical development sequence of transactional software changelog data using a skip-gram method with negative sampling and unsupervised nearest neighbors. We validate our approach by analyzing historical data from the software development changelog for more than ten years. Using multiple metrics, such as the normalized discounted cumulative gain at K (NDCG@K) and the hit ratio at K (HR@K), we achieved an average HR@K of 0.34 at the file level and an average HR@K of 0.49 at the package level across the three datasets. These results confirm the effectiveness of the FCP2Vec method in predicting the next change propagation from historical changelog data, addressing the identified research gap, and show a 21% better accuracy than in the previous study at the package level. [ABSTRACT FROM AUTHOR]

Despite the fact that Recommender Systems have been researched and developed for quite a while, they still hold some certain challenges, two of which are Cold Start and Data Sparsity. Being introduced as a savior, the Cross-domain Recommender system (CDR) owns the capacity to transfer knowledge across domains which makes it become the practical solution for the mentioned issues. In CDR approaches, the family of graph-based solutions is very effective, which builds graphs to illustrate the relationship between users, items, and other factors and learn their representations through graph representation learning. However, most graph-based approaches focus on extracting either domain-specific or domain-shared features and do not have the mechanism to prevent the transfer of private features, which can degrade the quality of vector representations. Moreover, the knowledge transfer process built on overlapping users makes the model biased toward these users, thus downgrading the performance on cold-start users. This paper proposes a meta-GRS framework that uses Graph Neural Network with Meta-Learning and Adversarial Learning for cross-domain recommendation. In meta-GRS, the representative user and item embeddings are improved thanks to the features extracted by the private and cross-domain graphs. Domain-shared features are learned under adversarial learning such that the domain discriminator is unable to determine whether the domain they came from can ensure the positive transfer. To optimize the model performance effectively, we use a Dynamic Weight Averaging algorithm to learn loss weights automatically. The model's parameters are optimized under the optimization-based meta-learning method provides our model the capability of generalization to the new users. Experiments on practical datasets illustrate that the meta-GRS leads the chart in the comparison of other state-of-the-art baselines in recommendation accuracy. [ABSTRACT FROM AUTHOR]

Recommender systems play an important role in providing users required information in a timely and effective manner. However, the cold-start problem of limited historical records of a new user in target domain makes it difficult to model user's comprehensive preferences. This severely affects the accuracy of recommendation. Although some meta-learning based approaches have alleviated the cold-start problem by learning well-generalized initial parameters for each user, they neglect user's information in source domains, and seem to be weak in providing each user suitable initial parameters separately. To tackle these challenges, we propose a novel cluster-based domain adaptive meta-learning model for cross-domain recommendation (CDAML). Specially, we utilize the adversarial cross-domain methods to introduce domain adaptation into the meta-learning framework, which can transfer domain-independent user preferences (i.e. intrinsic preferences) from source domains for improved recommendation in target domain via adversarial learning. Besides, we further design a soft-clustering based method to guide the globally shared parameter initialization in a finer granularity of cluster level, which not only contribute to avoid local optima, but also better transfer the shared knowledge among users with similar cross-domain preferences. Finally, comprehensive experiments are conducted on three real-world datasets to demonstrate the superior performance of CDAML compared with state-of-the-art recommendation methods. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a powerful data modeling tool, Heterogeneous Information Network (HIN) has been successfully used in auxiliary information exploitation to boost recommendation performance. For HIN based recommendation, it is challenging to extract and fuse useful features of user preferences and item attributes under different semantic paths in HINs. Existing methods leverage a pre-defined fusion function to integrate different semantics for recommendation, which cannot characterize the complex nonlinear interactions between users and items. In this paper, we present a general framework named MNRec, short for Meta-path fusion based Neural Recommendation, to extract and fuse user and item embeddings under different meta-paths for recommendation. Under the framework, we propose an instantiation of MNRec with Multi-Layer Perceptron (MLP) structure. It consists of two major steps, i.e., meta-path based heterogeneous network embedding and deep learning based rating prediction. Concretely, appropriate meta-paths are first designed according to domain knowledge. Then the embeddings of users and items are obtained through a meta-path and commuting matrix based heterogeneous network embedding method. Finally, in light of the powerful nonlinear modeling capabilities of deep neural networks, the learned embeddings under different meta-paths are integrated into a two-pathway MLP structure for rating prediction. Experimental results on three real-world datasets demonstrate the superiority and effectiveness of MNRec compared with state-of-the-art baselines in rating prediction. [ABSTRACT FROM AUTHOR]

This paper proposes a sequential rules-based recommendation system, called STS-Rec. It addresses the main drawbacks of sequential patterns mining approaches for POI (Point of interest) recommendation by considering both temporal and social influences to perform short-term recommendations. STS-Rec first transforms mobility data into location sequences. Then, it incrementally mines sequential recommendation rules in these sequences. In contrast with standard sequential recommenders, the proposal (1) discovers rules that tolerate locations' order variations by loosening the strict ordering constraint of location sequences, (2) builds a tree-based model to incrementally mine recommendation rules, and (3) supports short and long-term POI recommendation by using a user-defined window by extracting patterns that appear within a maximum number of consecutive locations. To take the temporal influence into account, STS-Rec adapts its mining strategy to include the temporal context in location data. Hence, the conventional rule mining problem is redefined to mine time-extended recommendation rules. An experimental evaluation conducted on two large-scale real check-in datasets from Gowalla and Brightkite shows that the proposed model outperforms two state-of-the-art sequential models in terms of accuracy and coverage. [ABSTRACT FROM AUTHOR]

Most hotel recommendation systems currently rely on text-based information or meta-data. We develop a deep network recommendation model with three modalities – picture, review, and scoring.We propose a unifified deep neural network including an embedding layer, pooling layer, and fully connected layer. Comparing with other algorithms, we verify its efficacy in improving travel recommendations based on the hotel data crawled from Ctrip and the major evaluation indicators. Our study contributes to the literature by building a knowledge model for tourist hotels based on the analysis of user-generated data and providing practical guidance for hotel managers and users. [ABSTRACT FROM AUTHOR]

Online services, ambient services, and recommendation systems take user preferences into data processing so that the services can be tailored to the customer's preferences. Associative rules have been used to capture combinations of frequently preferred items. However, for some item sets X and Y, only the frequency of occurrences is taken into consideration, and most of the rules have weak correlations between item sets. In this paper, we proposed a method to extract associative rules with a high correlation between multivariate attributes based on intuitive preference settings, process mining, and correlation distance. The main contribution of this paper is the intuitive preference that is optimized to extract newly discovered preferences, i.e., implicit preferences. As a result, the rules output from the methods has around 70% of improvement in correlation value even if customers do not specify their preference at all. [ABSTRACT FROM AUTHOR]

To alleviate the big data difficulties that have created a potential problem for many Internet users, it is necessary to filter, rank, and efficiently communicate the relevant information on the Web, where the diversity of possibilities is overwhelming. Recommender systems, which sift through enormous amounts of dynamically generated data to give consumers with personalised information and services, are able to overcome this problem. Bipartite graphs are currently generally used to store and understand this data due to its sparse nature. Data are mapped to a bipartite user-item interaction network where the graph topology captures detailed information about user-item associations, transforming a recommendation issue into a link prediction problem. Earlier, approaches for link prediction in bipartite graphs for various recommendation systems were developed, but the efficacy of the prediction methodology was not close to the standards required by real-time recommenders. So, the primary goal of this research is to offer an effective link prediction-based recommendation system that takes advantage of SVD++ along with K-nearest neighbors and reduces the system's error rate, leading to better outcomes. The proposed system is sorely tested on the MovieLens dataset and compared to some traditional recommendation methods. The results demonstrate that the suggested strategy exceeds all traditional approaches in terms of accuracy, and the actual suggestions are equally encouraging. [ABSTRACT FROM AUTHOR]

In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called “exposure” perspective, i.e., modeling how an item is exposed (provided) to a user. Most of the existing approaches use heuristic models or re-weighting strategy on observed ratings to mimic the missing-at-random setting. However, little research has been done to reveal how the ratings are missing from a causal perspective. To bridge the gap, we propose an unbiased and robust method called DENC (De-Bias Network Confounding in Recommendation), inspired by confounder analysis in causal inference. In general, DENC provides a causal analysis on MNAR from both the inherent factors (e.g., latent user or item factors) and auxiliary network’s perspective. Particularly, the proposed exposure model in DENC can control the social network confounder meanwhile preserve the observed exposure information. We also develop a deconfounding model through the balanced representation learning to retain the primary user and item features, which enables DENC generalize well on the rating prediction. Extensive experiments on three datasets validate that our proposed model outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Learning vector embeddings of users and items is the core of modern recommender systems. Recently the collaborative filtering recommender systems based on graph convolutional networks, which integrates the bipartite graph of user-item interaction into the embedding process, has achieved significant success. However, such feature as item-item interaction sequence is neglected in the bipartite graph, which limits the ability to model sequential orders for embedding of items. In this work, we propose a novel item-item interaction sequential graph to globally aggregate the hidden interactions sequence among all items. It is derived from the order of all user-item interactions and can give a supplement for user-item interaction modeling in CF. We also propose an item enhanced graph collaborative network (IEGCN) to mix item-item sequences with user-item interactions for collaborative filtering. We performed experiments on three open datasets, and IEGCN shows substantial improvements in recall and normalized discounted cumulative gain when compared with existing mainstream models. Further analysis verifies the importance of item-item sequence graph to improve the recommendation effect. [ABSTRACT FROM AUTHOR]

With the increase of open APIs appeared on the Web, reusing or combining these APIs to develop novel applications (e.g. Mashups) has attracted great interest from developers. However, to quickly find a suitable one among a huge number of APIs to meet a developer's requirement is basically a non-trivial issue. Therefore, a high-quality API recommendation system is desirable. Although a number of collaborative filtering methods have been proposed for API recommendation, their recommendation accuracy is limited and needs to be further improved. Based on the neural graph collaborative filtering technique, this paper proposes an API recommendation method that exploits the high-order connectivity between APIs and API users. To evaluate the proposed method, extensive experiments are conducted on a real API dataset and the results show that the proposed method outperforms the state-of-the-art methods in API recommendation. [ABSTRACT FROM AUTHOR]

Recommendation has become an inseparable component of many software applications, such as e-commerce, social media and gaming platforms. Particularly in collaborative filtering-based recommendation solutions, the preferences of other users are considered heavily. At this point, trust among the users comes into the scene as an important concept to improve the recommendation performance. Trust describes the nature and the strength of ties between individuals and hence provides useful information to improve the recommendation accuracy, particularly against data sparsity and cold start problems. The Trust notion helps alleviate the effect of these problems by providing additional reliable relationships between the users. However, trust information, specifically explicit trust, is not straightforward to collect and is only scarcely available. Therefore, implicit trust models have been proposed to fill in the gap. The literature includes a variety of studies proposing the use of trust for recommendation. In this work, two specific sub-problems are elaborated on: the relationship between explicit and implicit trust scores, and the construction of a machine learning model for explicit trust. For the first sub-problem, an implicit trust model is devised and the compatibility of implicit trust scores with explicit scores is analyzed. For the second sub-problem, two different explicit trust models are proposed: Explicit trust modeling through users' rating behavior and explicit trust modeling as a link prediction problem. The performances of the prediction models are analyzed on a set of benchmark data sets. It is observed that explicit and implicit trust models have different natures, and are to be used in a complementary way for recommendation. Another important result is that the accuracy of the machine learning models for explicit trust is promising and depends on the availability of data. [ABSTRACT FROM AUTHOR]

Collaborative filtering is a popular approach for building an efficient and scalable recommender system. However, it has not unleashed its full potential due to the following problems. (1) Serious privacy concerns: collaborative filtering relies on aggregated user data to make personalized predictions, which means that the centralized server can access and compromise user privacy. (2) Expensive resources required: conventional collaborative filtering techniques require a server with powerful computing capacity and large storage space, so that the server can train and maintain the model. (3) Considering only one form of user feedback: most existing works aim to model user preferences based on explicit feedback (e.g., ratings) or implicit feedback (e.g., purchase history, viewing history) due to their heterogeneous representation; however, these two forms of feedback are abundant in most collaborative filtering applications, can both affect the model, and very few works studied the simultaneous use thereof. To solve the above problems, in this study we focus on implementing decentralized probabilistic matrix factorization for privacy-preserving recommendations. First, we explore the existing collaborative filtering algorithms and propose a probabilistic matrix co-factorization model. By integrating explicit and implicit feedback into a shared probabilistic model, the model can cope with the heterogeneity between these two forms of feedback. Further, we devise a decentralized learning method that allows users to keep their private data on the end devices. A novel decomposing strategy is proposed for users to exchange only non-private information, in which stochastic gradient descent is used for updating the models. Complexity analysis proves that our method is highly efficient with linear computation and communication complexity. Experiments conducted on two real-world datasets FilmTrust and Epinions show that our model gains a guarantee of convergence as the RMSE decreases quickly within 100 rounds of iterations. Compared with the state-of-the-art models, our model achieves lower model loss in rating prediction task and higher precision in item recommendation task. [ABSTRACT FROM AUTHOR]

With advances in machine learning, the application of financial services has become increasingly intelligent. The artificial intelligence financial advisory and other customized services have been launched in the market. However, when facing new financial products, recommendation models may not possess sufficient training data, which is called the cold-start problem. To address this problem, we present the few-shot learning model, which aims to learn a new category through a limited number of samples by transferring the past experiences and knowledge to the learning of new categories. We trained our model by using considerable past transaction data and applied a customized bias to adjust the predicted result for each investor. We then tested our model on new stock items by applying few-shot learning and testing. The results of this study indicate that training the proposed network with a customized bias can improve its prediction accuracy, especially for new items. Moreover, the proposed model can transform past experience into knowledge. When new data with only a few available samples are input into the proposed model, it can learn and make predictions and thus achieve few-shot learning. [ABSTRACT FROM AUTHOR]

Summary: With the development of Internet technology and the cloud service industry, an increasing number of application programming interfaces (APIs) hosted in the cloud has been made publicly available. To facilitate cloud service APIs vendors and buyers, some large‐scale software cloud directory platforms have been established. Nevertheless, it is difficult for users to choose for renting from a massive number of cloud service APIs with similar functionalities in a software cloud directory platform. Recent efforts in building cloud service APIs recommender systems can help address this challenge. Relevant existing recommendation approaches are designed based on requirement election techniques to identify users' preferences to the quality of service (QoS) of the APIs. In particular, users' preferences are mainly obtained through their self‐description, in which users sometimes cannot accurately and completely express their preferences. In this article, we propose SVD‐APIR, a singular value decomposition (SVD)‐based behavior‐aware cloud service APIs recommendation approach for large‐scale software cloud directory platforms. In SVD‐APIR, users' historical behavior information is captured and APIs' association information is analyzed to identify the users' potential preferences to the APIs with specific QoS. A unified SVD model is utilized to prioritize the users preferred APIs. Experimental evaluation results conducted on WS‐Dream dataset demonstrate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

For the recommendation domain, most of the existing integrated graph neural network (GNN)-based architectures have still much focused on encoding the associated extra side information in forms of heterogeneous information network (HIN). Then, it is simultaneously utilized in multiple fine-tuning processes to effectively learn preferences from user–item interaction data which might require tremendous computational efforts. In addition, these approaches have also failed to incorporate with previous learnt and transferable knowledge from pre-trained models to better fine-tune for recommendation task. To meet these challenges, in this paper we propose a novel heterogeneous graph neural architecture, named: PreHIN4Rec. The proposed PreHIN4Rec is considered as the graph pre-training approach for leveraging the performance of recommendation task in both accuracy and scalability aspects. In general, our proposed PreHIN4Rec is designed to efficiently preserve both heterogeneous schematic and local structural latent features of user–item interactions in forms of HINs. It can effectively support to better fine-tune for achieving remarkable improvements in recommendation tasks through integrating with existing recommendation frameworks. The extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed PreHIN4Rec model in comparing with recent state-of-the-art GNN-based recommendation baselines. [ABSTRACT FROM AUTHOR]

• It provides a new data representation, named as hyper-classes representation, aiming to address the limitations in the attribute-centered calculation. • Weighting function is constructed as piecewise functions with the HC representation. • The hyper-class representation is able to provide truly useful reference information for recommendation systems and makes recommendations much better than existing algorithms. Data representation is usually a natural form with their attribute values. On this basis, data processing is an attribute-centered calculation. However, there are three limitations in the attribute-centered calculation, saying, inflexible calculation, preference computation, and unsatisfactory output. To attempt the issues, a new data representation, named as hyper-classes representation, is proposed for improving recommendation. First, the cross entropy, KL divergence and JS divergence of features in data are defined. And then, the hyper-classes in data can be discovered with these three parameters. Finally, a kind of recommendation algorithm is used to evaluate the proposed hyper-class representation of data, and shows that the hyper-class representation is able to provide truly useful reference information for recommendation systems and makes recommendations much better than existing algorithms, i.e., this approach is efficient and promising. [ABSTRACT FROM AUTHOR]

In today's Internet, recommender systems play an indispensable role in helping users discover items of interests, such as products, books, movies and so on. However, a higher recommendation accuracy is commonly at the cost of more disclosure of user privacy. Thus, a wider adoption of recommender systems poses significant security and privacy concerns to users. In this article, we propose a light-weight privacy-preserving framework called RAP for recommender systems, which can protect user privacy while still ensuring a high recommendation accuracy. Instead of directly sending users’ private ratings to the recommender, users first conduct a local perturbation operation on private ratings, and then send the perturbed ratings to the recommender. The recommender can run recommendation algorithms directly over the perturbed ratings and return the results to users. Different from crypto-based methods, our perturbation and de-perturbation methods are linear operations. Thus, RAP is light-weight and highly efficient in privacy protection. To be more rigorous, we formally prove that the order of recommendation accuracy will not decrease when our RAP framework is applied to any MF (Matrix Factorization)-based recommender systems. We also derive the closed-form expression for the degree of privacy preservation of our framework. Finally, we conduct extensive evaluations using large-scale real-world datasets to verify the effectiveness of our RAP framework and compare with other baseline algorithms. The results show that our RAP framework can improve the degree of privacy preservation from zero to over 0.5 for the Movielens dataset and 4 for the Jester dataset, and still maintain the approaching level of recommendation accuracy. [ABSTRACT FROM AUTHOR]

Classification of label‐specific users' diversified interests and incorporating social media with news media to address popular news articles which is the most formidable task in popularity‐based personalized news recommendation systems (PPNRS). To bring personalization to PPNRS, many remarkable features have to be considered from users user profiles to classify their interest. In this article, 13,346 features per user considered to classify their interest for 15 labels using multi‐label convolutional neural network (MLCNN). The efficiency of MLCNN model highly depends on its architecture through the tuning of its hyperparameters. Generally, researchers manually designed a constant CNN architecture for every label and verified the effectiveness, but this leads to an additional complexity as well as large computational resources were consumed. Moreover, designing the structure for all 15 labels leads to an increase in the network structure exponentially with an increase in labels. Hence, in this manuscript, MLCNN architectures optimized by implementing a novel approach modified genetic algorithm (MGA) with the help of introducing four novel crossover operators to strengthen CNN performance for users interest classification. Further, for the recommendation process, the label‐specific news articles were clustered from social media Facebook and Twitter feeds, and then most popular news articles determined from clusters along with label‐specific breaking news articles rendered from news feeds concerning users' interest. In addition to that, the reliability of the news articles also validated for recommendation process. The experimental result precisely proves that the proposed approach MGA attained an accuracy of 89.64%, 90.56%, 90.41%, and 91.79% for classifying users label specific interest and label‐wise recommendation accuracy attained 93.3%, 90%, 90% from Twitter, Facebook, and also from Newsfeed respectively. [ABSTRACT FROM AUTHOR]