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In the past two decades, recommendation system has been successfully applied to many e-commerce companies and is a ubiquitous part of today online entertainment. However, many single-domain recommendations suffer from the sparsity problems due to a lack of sufficient interactive data. In fact, user behaviors from different domains are usually relevant. Therefore, cross-domain ideas have been proposed to help alleviate the data sparsity issue in traditional single-domain recommender systems. Motivated by this, we design a deep selective learning network (DSLN) in this paper, for the scenario when domains have minimum or no common users DSLN firstly exploits reviews to profile the preference of users and characteristic of items. Then it selects useful user or item information from the auxiliary domain and transfers it to the target domain to solve the negative transfer problem, even though there may be no overlapping users or items between these two domains. In DSLN model, the selection of useful information is realized by the de-noising auto-encoder (DAE), which is shared between the auxiliary and target domains. By minimizing the reconstruction error of the DAE, on the one hand, only the useful information can be selected from the auxiliary domain; on the other hand, the latent representation of users and items in two domains can be learned. Our experiments on three cross-domain scenarios with different sparsity of Amazon review dataset show that, our proposed model gains 0.58% to 18.16% relative improvement compared to single-domain recommendation models, and from 1.05% to 19.4% relative improvement compared to cross-domain recommendation models. • Profile the preference of users and the characteristic of items from reviews. • Fit for the situation where there are no overlapping entities between two domains. • Propose an automatic selection mechanism to select informative entities. • Use a regularized constraint to avoid a trivial solution and select more entities. • Exploit supervised information to select entities with high predictive confidence. [ABSTRACT FROM AUTHOR]

The cold-start problem has been of great concern in the recommendation domain. To address this problem, meta-learning frameworks have been widely adopted due to their fast adaptation to new tasks. However, existing meta-learning-based methods always use higher-order graph structures to obtain global user preferences but neglect to consider local preferences at different rating levels in a fine-grained manner. Since differences in user ratings on items truly reflect differences in users' local preferences, we propose a relation-propagation meta-learning on explicit preference graph for cold-start recommendation (RPMLG-Rec) to improve the generalization performance of existing meta-learning-based methods. Specifically, we focus on capturing the relationships between local preferences. First, our RPMLG-Rec approach concatenates different local preferences to form the nodes of local user preference and further constructs an explicit preference graph. Second, the relationships between local preferences, including intraclass commonality and interclass uniqueness, are used to guide the propagation of relationships in the explicit preference graph with graph convolutional networks and produce more distinguishable local preference nodes. Third, the precise global user preference is obtained with an attention mechanism. Finally, prior knowledge is learned based on a set of training tasks and quickly adapted to make recommendations for new tasks, following the optimization-based meta-learning training strategy. To the best of our knowledge, this is the first time that the relationships between local user preference nodes have been explicitly considered in cold-start recommendation. In addition, we conducted extensive experiments on two real-world datasets, and the experimental results demonstrate the effectiveness of our proposed framework. [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]