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Network representation learning can effectively solve the problem of data sparsity in recommendation. In this paper, LINE and DeepWalk in network representation learning were improved, and a hybrid recommendation algorithm was proposed to be applied to movie recommendation scene. The new algorithm generates three low dimensional feature vectors by learning user preference feature, user aversion feature and similar user feature. Three low dimensional feature vectors are linearly combined to form a user representation vector, and cosine similarity is used as the similarity index to recommend the movies associated with similar users to target users. Experimental results show that, compared with the suboptimal algorithm, the accuracy and Fl index of the proposed algorithm are improved by 12% and 7% respectively on MovieLens dataset, and 16% and 18% respectively on MovieTweetings dataset. The recommendation algorithm based on multi-dimensional feature representation learning proposed in this paper has significant advantages in movie recommendation scenes. [ABSTRACT FROM AUTHOR]

Focusing on problems of the accuracy recommendation system that the recommended commodity type is relatively single, and commodities are mostly popular goods and lack of freshness, the novelty recommendation is gradually gaining attention. However, current researches don't combine item features when designing algorithms, which make them unable to distinguish and select items with higher novelty for different users. In order to improve the performance of the recommendation system, this paper improved the method based on random walk and designed a new recommendation algorithm by fusing novelty features. This algorithm further analyzed features of items and gave the formal definition of the novelty from perspectives of user interest expansion and prediction. This paper analyzed user demands, constructed new transition probability, generated personalized recommendation lists and improved the novelty of the lists. The experimental results show that the proposed algorithm has less influence on the accuracy than existing methods and has significant improvement on novelty indexes. It concludes that by fusing novel features, this system can improve the recommendation contents effectively while taking into account the accuracy. [ABSTRACT FROM AUTHOR]

When modeling users' preferences, current researches of group recommendation usually ignored the mutual influence between group preference and individual preference and the problem of modeling initialization. To address these issues, this paper proposed a new group recommendation model called ranking based hybrid deep tensor factorization model, namely R-HDTF model. First of all, this paper developed a hybrid deep learning-based initialization method, which utilized deep denoising autoencoder to pretrain the initial values of the parameters for the R-HDTF model. Then, it proposed a pairwise tensor factorization model to capture the correlation among group, individual and item. Finally, it used the Bayesian personalized ranking( BPR) metric to optimize the loss objective function of tensor factorization and learn the parameters of the proposed model. Experimental results on real data sets show that the performance of the proposed algorithm outperforms traditional group recommendation algorithm. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the recommendation model cannot mine the diversity of user interest and capture the order information between the sequence of user behavior, and the interaction occurs at the element level rather than between the feature vectors, etc., based on multiple attention mechanism and location information, this paper proposed the x DMAL FM. Firstly, it extracted the feature depth from different subspaces by using the multi-head attention mechanism, and then location information could capture the sequential relationship between user behavior sequences. Finally, it used three public datasets to conduct comparative experiments and the AUC index to evaluate the results. The experimental results show that the proposed algorithm has better recommendation performance than the xDeepFM model and show the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, as deep learning has achieved good results in many fields, deep learning has also begun to be applied to recommendation systems, such as NFM models and DeepFM models that use deep learning technology to capture high-level feature interactions. However, considering the changes in the external environment and internal perception, the user' s interest should also change dynamically over time, and the combination based on the original features may not necessarily learn effective feature interaction. This paper attempted to build a new model FG_DRFwFm, which could learn the interaction of low-level and high-level features of multiple feature domains and dealt with long-term changes in user interest. It constructed the training features by constructing new features based on the original features and splicing them together, which could better learn effective feature interaction. Finally, the proposed model compared the recommendation effect with multiple advanced CTR algorithms on the MovieLens data set. The experimental results show that the proposed model achieves better results. [ABSTRACT FROM AUTHOR]

In order to solve the problem that users deep interests and preferences cannot excavate in the recommendation. algorithm, which leads to low extraction accuracy and high time complexity of similar users clustering accuracy, this paper proposed a clustering optimization recommendation algorithm (MT-QRPD) based on the evaluation quantization model to optimize differential evolution. Firstly, it used the feature timing of BiGRU network and the strong local feature effectiveness of CNN network to extract the comment depth features, and used the multi-dimensional semantic feature screening of multi-head attention mechanism to mine the comment depth semantic features. Then, through the nonlinear transformation of the multi layer perceptron carried out the multi-feature fusion to achieve accurate quantification. Finally, the algorithm optimized the clustering of similar users with PCA, and completed the project recommendation. The result of experiments shows that the pro posed algorithm can improve the accuracy, time complexity and recommendation performance. [ABSTRACT FROM AUTHOR]

In order to solve the problem of data sparsity in traditional recommendation algorithms, many researchers at home and abroad have proposed corresponding recommendation algorithms. However, most of these algorithms have not achieved good recommendation results in personalized recommendation. Therefore, this paper proposed a recommendation algorithm based on improved filling method and multi-weight similarity. Firstly, the algorithm filled missing values and reduced data dimension by improved filling method, then calculated use r trust degree and user association degree of bipartite graph respectively, and finally used multi-weight factor to combine the two similarities. Based on this, this algorithm obtained neighbor users according to similarity and made recommendation to target users. The experimental results show that the MAE of proposed algorithm is superior to other recommendation methods in the case of sparse data and personalized recommendation. [ABSTRACT FROM AUTHOR]

To solve the problem of lacking user ratings and inaccuracy of user topics model for danmaku video website, this paper proposed a danmaku video recommendation algorithm combing topic model and collaborative filtering ( DRCIT). Firstly, it constructed an implicit rating matrix with user behavior, and obtained the similarity of project rating. Secondly, it established an LDA topic model to generate project topics and calculate the similarity of project topics. Thirdly, it generated a comprehensive project similarity, and calculated the user-project prediction score. Then, through sigmoid penalty, it established the new similarity matrix of project topics and got user-project topic similarity combined with user history behavior. Finally, it took user-project topic similarity as weight to multiply with prediction score to get the final prediction score. The comparison experiments show that the algorithm can get reasonable recommendation results and improve the accuracy of recommendation. [ABSTRACT FROM AUTHOR]

The traditional collaborative filtering recommendation algorithm has the problems of low timing and high computational complexity of similar users with sparse data when there are too many users. According to the above problem, this paper proposed a spectral clustering optimization recommendation algorithm combining the minimum hash signature and temporal model prediction. Firstly, it used the MHS and Levenshtein distance measure to extract similar users from user-item scoring matrix. Then it used AIM-RT prediction algorithm with weight fitting to complete sparse matrix of similar users. Finally,it carried out similar user optimization by combining spectral clustering to find the optimal set of similar users and completed the final recommendation. Experimental analysis and verification show that the proposed recommendation algorithm can improve the overall recommendation performance in terms of computational complexity, scoring prediction accuracy, data missing filling and other aspects. [ABSTRACT FROM AUTHOR]

Based on the traditional personalized recommendation algorithm, this work presented a random walk recommendation algorithm using multi-weight similarity. In order to make up the absence of the influence among social networks, popular items and common scoring items in traditional collaborative filtering algorithms, this paper introduced a gravity formula, which could calculate user similarity in social networks. In addition, this work used weight factors to combine the gravity formula method with the traditional collaborative filtering algorithms. Furthermore, it also added the random walk algorithm for recommendation functions. The experimental results show that the proposed algorithm has better recommendation performance by comparing with other kinds of recommendation algorithms. [ABSTRACT FROM AUTHOR]

Recommender systems can effectively reduce the information overload by recommending items that users may be interested in. Euclidean-embedding-based collaborative filtering methods map users and items to a unified latent space, which is one of the most important methods to build a recommender system. However, traditional Euclidean-embedding-based collaborative filtering methods only consider the low-order interaction between user latent feature vectors and item latent feature vectors, and cannot efficiently model the complex interaction behavior between users and items in the real world. In this paper, we propose a deep-Euclidean-embedding-based collaborative filtering algorithm, which utilizes deep learning technology to learn the high-order and nonlinear interaction function between user latent feature vectors and item latent feature vectors. This can model the complex interaction behavior between users and items. Experimental results on real-world datasets show that our proposed algorithm outperforms traditional collaborative filtering algorithms. [ABSTRACT FROM AUTHOR]

The current trust-based recommendation algorithms did not fully exploit the trust relationship between users and it lacked reasonable trust transitivity rules, which greatly affected the reliability and accuracy of the recommendation algorithm. Aiming at the above problems, this paper combined user rating data with the user's social relationship to build a trust transitivity model, and proposed a recommendation algorithm based on trust transitivity. Firstly, the algorithm used the score data to calculate the implicit direct trust relationship of the users in the trust transitivity model. Secondly, it solved the indirect trust relationship of multiple trust transitivity chains by solving the ordered weighted average operator. Finally, it converge the user's trust and similarity into comprehensive similarity for predictive recommendation. The experimental results show that the proposed algorithm can effectively improve the recommendation quality of the system. [ABSTRACT FROM AUTHOR]

Aiming at the problem of defects in the accuracy of recommendation and data sparse processing for recommendations made by explicit feedback information. This paper introduced implicit feedback information, designed and implemented a multi-dimensional recommendation algorithm( iMCF) based on implicit feedback. The algorithm included three dimensional information of user, project and implicit feedback. For the first two dimensional information, it used the similarity of cloud model to model. For the information of implicit feedback, it mainly used the probabilistic matrix decomposition model to deal with. Afterwards, it balanced the prediction scores obtained from the three dimensions according to the weights, and obtained the final prediction score and made recommendation. The experimental data shows that the algorithm's performance in terms of recall rate and accuracy is significantly improved compared to other algorithms, and it is suitable for big data environments. [ABSTRACT FROM AUTHOR]

Tradition al personalized recommendation algorithm generally suffers from the problem of data sparseness, which affects the accuracy of recommendation. The Slope One algorithm is simple and efficient, but the algorithm is only based on the user - project score matrix to analyze the data, ignoring the type characteristics of the project and the user's preference for the type of the project. In order to solve the above problems, this paper proposed the LR-Slope One algorithm. Firstly, it constructed the user's preference matrix based on user project score matrix and project type information. Secondly, it calculated the weight of each type by linear regression model and optimized the weight by random gradient descending algorithm. Finally, it predicted the score combined by Slope One, filled the scoring matrix, which improved the quality of recommendation. Experimental results show that the proposed algorithm improves the accuracy of recommendation and alleviates data sparseness effectively. [ABSTRACT FROM AUTHOR]

This paper proposed an approach called RQE-EDKL (recommendation quality evaluation based on empirical distribution and KL divergence) to evaluate the recommendation quality based on empirical distribution and KL divergence. QE-EDKL firstly made use of historical user-item data to produce the historical usage probability distribution of items at different quantities. Secondly, it calculated the KL divergence based on the distributions of the historical usage probability and the usage probability of different recommendations. Thirdly, it regarded the recommendation with the minimum KL divergence as with the best quality and is recommended to the user. Experiments on TalkingData App data sets demonstrate that RQE-EDKL can effectively improve the quality of recommended results of collaborative filtering significantly on both accuracy and diversity. [ABSTRACT FROM AUTHOR]

Aiming at the problem of ignores the users interest in the key words and the data sparseness in traditional collaborative filtering algorithm. This paper proposed a collaborative filtering recommendation algorithm combined with the user interest degree clustering, and used user ratings for projects and extracting keywords from item attributes. It proposed a new rating frequency-inverse item frequency algorithm. According to the target users' scoring frequency for a key word and the frequency of the keyword being evaluated by all users, it got users' preferences for keywords, formed user preference matrix, and clustered on the basis of this matrix. Then it used logistic function to get users' interest in projects, cleared user preferences and found similar users of target users in the clusters. Then extracted N items from neighbors' preferences, and recommended users. Experimental results show that the algorithm accuracy rate is always better than the traditional algorithm. It's more accurate to judge the user interest, alleviating the problem of data sparseness, and effectively improves the accuracy and efficiency of recommendation. [ABSTRACT FROM AUTHOR]

This paper proposed a linear regression algorithm to integrate collaborative filtering based on the data sparse influence of the traditional collaborative filtering algorithm. Firstly, it built a similarity matrix between the user and the project based on the user’s rating of the project, as well as the user and the project’s own characteristics. Secondly, based on the similarity matrix, it selected the user and project nearest neighbor set. It predicted the score that the users had graded respectively by the way of collaborative filtering algorithms based on the user and the project. And it would take the difference between predicted scores and the real scores as features to generate new training data, and regarded the new training data as the input of the linear regression model. Finally, according to the training model, it could predict the unknown score, and used the top-N algorithm to generate the recommended list. It conducted the experiment on the MovieLens data set. The experimental result shows that the proposed accuracy of the new algorithm improves compared with the traditional collaborative filtering algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the unreasonable allocation of resource in the recommendation algorithm of bipartite network, and in order to enrich the diversity of recommended results and promote the recommendation of unpopular items, this paper proposed the algorithm for bipartite graph network structure, which used differential path weight to change resource delivery. It changed the first stage of resource transfer rules by user similarity to construct path weights and made the resources flow more towards the user nodes similar to the target users. Through the consideration of constructing path weight of the articles attributes’ similarity, the second stage resources were more likely to flow to the objects which had similar attributes with the target users’ purchased products. Experimental results show that the proposed algorithm improves the combination property of recommendation, and better solves the related problems in recommendation, which outperforms other popular network structure algorithms. [ABSTRACT FROM AUTHOR]

The efficiency of traditional user-based collaborative filtering (user-based CF) recommendation algorithm is reduced with data increasing. This paper proposes a recommendation algorithm based on community detection (RACD) in bipartite networks by introducing bipartite network community detection theory into user-based CF recommendation algorithm. Firstly， the user-item rating matrix is mapped into user-item bipartite network. Then， the community information of each user is obtained by using RACD to divide the user-item network. Finally， the items are recommended to the target user according to other users in the same community. Experiments on real-world classic network datasets show that the RACD can effectively improve real-time recommendation efficiency of the recommendation system. [ABSTRACT FROM AUTHOR]