Haifeng Xia
ABSTRACT
Recommender system plays an important role to provide people with personalized information based on their history records. However, it is still a challenge to capture the preference of users accurately due to the sparsity of rating data and the heterogeneity of review data. In this paper, we propose a hybrid deep collaborative filtering model that jointly learns latent representations from ratings and reviews. Specifically, the model learns the rating feature and textual feature based on ratings and reviews simultaneously. Two embedding layers are employed to learn rating feature for users and items based on the user and item interactions, and two attention-based GRU networks learn context-aware representation from user and item reviews. Then a gating mechanism is used to leverage contributions from rating feature and textual feature. Experimental results on six real-world datasets demonstrate the superior performance of the proposed method over several state-of-the-art methods. Moreover, the keywords in reviews can be highlighted to interpret the predictions with the attention mechanism.
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Index Terms
- Leveraging Ratings and Reviews with Gating Mechanism for Recommendation
Recommendations
DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningDespite the great success of many matrix factorization based collaborative filtering approaches, there is still much space for improvement in recommender system field. One main obstacle is the cold-start and data sparseness problem, requiring better ...
Personalized Dynamic Knowledge-Aware Recommendation with Hybrid Explanations
Database Systems for Advanced ApplicationsAbstractExplainable recommendation is attracting more and more attention in both industry and research communities. While some existing models utilize reviews for improving the performance of recommender systems, most of them assume that user’s preference ...
Mutual Self Attention Recommendation with Gated Fusion Between Ratings and Reviews
Database Systems for Advanced ApplicationsAbstractProduct ratings and reviews can provide rich useful information of users and items, and are widely used in recommender systems. However, it is nontrivial to infer user preference according to the history behaviors since users always have different ...
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