Motivation: （基于结合评分数据和review数据）
本文认为以前的方法是静态和单独考虑user和item review的数据，而忽视了他们隐因子之间的联系；
本文认为以前的方法不能高效的统一评分和review数据；
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| DAML: Dual Attention Mutual Learning between Ratings andReviews for Item Recommendation | |||||||||||
| 文献作者 | Bo Du | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| 摘要描述 | |||||||||||
| Despite 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 solutions. Recent studies have attempted to integrate review information into rating prediction. However, there are two main problems: (1) most of existing works utilize a static and independent method to extract the latent feature representation of user and item reviews ignoring the correlation between the latent features, which may fail to capture the preference of users comprehensively. (2) there is no effective framework that unifies ratings and reviews. Therefore, we propose a novel dual attention mutual learning between ratings and reviews for item recommendation, named DAML. Specifically, we utilize local and mutual attention of the convolutional neural network to jointly learn the features of reviews to enhance the interpretability of the proposed DAML model. Then the rating features and review features are integrated into a unified neural network model, and the higher-order nonlinear interaction of features are realized by the neural factorization machines to complete the final rating prediction. Experiments on the five real-world datasets show that DAML achieves significantly better rating prediction accuracy compared to the state-of-the-art methods. Furthermore, the attention mechanism can highlight the relevant information in reviews to increase the interpretability of rating prediction. | |||||||||||
