Liu Tongcun, Lou Siyuan, Liao Jianxin, Feng Hailin
IEEE Trans Neural Netw Learn Syst. 2022 Jun 2;PP. doi: 10.1109/TNNLS.2022.3177611.
Existing review-based recommendation methods learn a latent representation of user and item from user-generated reviews by a static strategy, which are unable to capture the dynamic evolution of users' interests and the dynamic attraction of items. Here, we propose a dynamic and static representation learning network (DSRLN) to improve the rating prediction accuracy by exploring fine-grained representations of users and items. Specifically, we built DSRLN with a dynamic representation extractor to model the dynamic evolution of users' interests by exploring the inner relations of an interaction sequence, and with a static representation extractor to model the users' intrinsic preferences by learning the semantic coherence and feature strength information from reviews. To identify the different influences of dynamic and static features for different users, a personalized adaptive fusion module was designed using a weighted attention mechanism. Extensive experiments on five real-world datasets from Amazon demonstrated the superiority of the proposed model, and the additional ablation studies verified the effectiveness of the components designed in the DSRLN model.
现有的基于评论的推荐方法通过静态策略从用户生成的评论中学习用户和物品的潜在表示，这无法捕捉用户兴趣的动态演变和物品的动态吸引力。在此，我们提出一种动态和静态表示学习网络（DSRLN），通过探索用户和物品的细粒度表示来提高评分预测准确性。具体而言，我们构建了具有动态表示提取器的DSRLN，通过探索交互序列的内部关系来建模用户兴趣的动态演变，并构建了具有静态表示提取器的DSRLN，通过从评论中学习语义连贯性和特征强度信息来建模用户的内在偏好。为了识别动态和静态特征对不同用户的不同影响，使用加权注意力机制设计了一个个性化自适应融合模块。在来自亚马逊的五个真实世界数据集上进行的大量实验证明了所提出模型的优越性，额外的消融研究验证了DSRLN模型中设计的组件的有效性。
