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In the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most commonly applied and successful recommendation approaches, which refers to using the preferences of groups with similar interests to recommend information to other users. Recently, in addition to the traditional matrix factorization techniques, deep learning methods have been proposed to learn more abstract and higher-level representations for recommendation. However, most previous deep recommendation methods learn the higher-level feature representations of users and items through an identical model structure, which ignores the different characteristics of the user-based and item-based data. In addition, the rating matrix is usually sparse which may result in a significant degradation of recommendation performance. To address these problems, we propose a representation learning method with Collaborative Autoencoder for Personalized Recommendation (CAPR for short). In this method, user-based and item-based feature representations are learned by two different autoencoders for capturing different features of the data. Meanwhile, items' attributions are combined into the feature representations with semi-autoencoder for alleviating the sparsity problem. Extensive experimental results confirm the effectiveness of our proposed method compared to other state-of-the-art matrix factorization methods and deep recommendation methods. • Two different autoencoders are used to capture characteristics for users and items. • Manifold regularization is integrated into autoencoder for user's features learning. • The comprehensive experiments evaluate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Recent advances in interactive recommender systems (IRS) have received wide attention due to its flexible recommendation strategy and optimization for users' long-term utility. Considering this interaction paradigm of IRS, researchers have made some attempts to incorporate reinforcement learning (RL) models into IRS, because of the excellent ability of RL in long-term optimizing and decision making. However, data sparsity is an intractable problem most IRS urgently need to address. Although a small amount of work has exploited reviews to address data sparsity, they ignored the varying importance of items for modeling the user. In addition, most existing RL-based approaches suffer from decision-making difficulties when the action space becomes large. To solve above problems, in this work, we present a Review-enhanced Deep Reinforcement Learning model (REDRL) for interactive recommendation. Specifically, we utilize text reviews, combined with a pretrained review representation model to acquire item review-enhanced embedding representations. Then we formalize the recommendation problem as a Markov Decision Process (MDP), and exploit deep reinforcement learning (DRL) to model the interactive recommendation. Notably, we introduce a multi-head self-attention technique to capture distinct importance of various items in the sequence behavior, which is overlooked by existing work when modeling the user preference. In this way, we can model long-term dynamic preferences of users accurately and discriminately for comprehensive interactive recommendation. Moreover, we subtly combine the semantic structure information in the user–item bipartite graph with meta-paths in heterogeneous information networks (HIN), to filter some irrelevant items and obtain candidate items dynamically. By this means, the size of the discrete action space is effectively reduced from a new anger. The experimental results based on three benchmark datasets demonstrate the efficiency of our method with significant improvement over state-of-the-art. • Mining information in reviews and interaction data via the pretrained model. • Modeling long-term dynamic preferences of users accurately and discriminately. • Filtering irrelevant items and getting candidate items dynamically from a new anger. • Better interactive recommendation based on deep reinforcement learning. [ABSTRACT FROM AUTHOR]

In various recommender systems, ratings and reviews are the main information to show user preferences. However, recommendation models that only use ratings, such as collaborative filtering, are vulnerable to data sparsity. And models only using review information will also suffer from the sparsity of reviews. On one hand, most ratings and reviews are interrelated and complementary, reviews may explain why a user gives a high or low rating to an item. On the other hand, ratings and reviews are numerical and textual information, respectively, and they reflect the preference of the user from a coarse-grained level and a fine-grained level A user may comment positively about some aspects of an item, even he gives a very low score to this item. There are specific information among each of them because of their heterogeneity. Therefore, it is possible to learn more accurate representation of users and items by effectively integrating ratings and text reviews from different views, that is, shared-view and specific-view. In this paper, we propose a Shared-view and Specific-view Information extraction model for Recommendation (SSIR), which integrates the information from reviews and interaction matrix to predict ratings Our model has two key components, including shared-view information extraction and specific-view exploitation. From the perspective of shared-view, SSIR jointly minimizes the loss of confusion adversarial and rating prediction loss to extract the shared information from reviews and user–item interaction matrix. For the specific-view part, SSIR applies orthogonal constraints on shared-view and specific-view modules to extract the discriminative features from reviews and interaction data. We fuse the features extracted from these two views to predict the final ratings. In addition, we use auxiliary reviews to deal with the sparsity problem of reviews. Experimental results on eight datasets show the effectiveness and robustness of our method, which could adapt to the recommendation scenarios with fewer reviews and ratings. • Mining information in reviews and interaction data from shared and specific views. • Utilizing the confusion adversarial loss to extract the shared features. • Enforcing orthogonal constraints to extract the specific features. • Shared-view information and specific-view information are synergized to recommend. [ABSTRACT FROM AUTHOR]

Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, pure NCF models can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item embedding dimensions. To address these problems, we propose a novel Knowledge graph enhanced Neural Collaborative Recommendation (K-NCR) framework, which effectively combines user–item interaction information and auxiliary knowledge information for recommendation task into three parts: (1) For items, the proposed propagating model learns the representation of item entity. It recursively aggregates information from its multi-hop neighbours in KG, and employs an attention mechanism to discriminate the importance of the relation type to mine users' potential preferences. (2) For users, another heterogeneous attention weights are leveraged to strengthen the embedding learning of users. (3) The user and item embeddings are then fed into a newly designed two-dimensional interaction map with convolutional hidden layers to model the complex pairwise correlations between their embedding dimensions explicitly. Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our K-NCR framework. • An end-to-end knowledge graph enhanced neural recommendation is proposed. • An embedding propagation-based entity embedding framework is built. • An outer product based convolutional neural correlations framework is presented. [ABSTRACT FROM AUTHOR]