Li Zheng, Jin Di, Yuan Ke
College of Computer and Information Engineering, Henan University, Kaifeng, 475004, Henan, China.
Henan Engineering Laboratory of Spatial Information Processing, Henan University, Kaifeng, 475004, Henan, China.
Sci Rep. 2023 Aug 18;13(1):13454. doi: 10.1038/s41598-023-40633-4.
In recommender systems, user reviews on items contain rich semantic information, which can express users' preferences and item features. However, existing review-based recommendation methods either use the static word vector model or cannot effectively extract long sequence features in reviews, resulting in the limited ability of user feature expression. Furthermore, the impact of different or useless feature interactions between users and items on recommendation performance is ignored. Therefore, we propose an attentional factorization machine with review-based user-item interaction for recommendation (AFMRUI), which first leverages RoBERTa to obtain the embedding feature of each user/item review, and combines bidirectional gated recurrent units with attention network to highlight more useful information in both user and item reviews. Then we adopt AFM to learn user-item feature interactions to distinguish the importance of different user-item feature interactions and further to obtain more accurate rating prediction, so as to promote recommendation. Finally, we conducted performance evaluation on five real-world datasets. Experimental results on five datasets demonstrated that the proposed AFMRUI outperformed the state-of-the-art review-based methods regarding two commonly used evaluation metrics.
在推荐系统中，用户对商品的评论包含丰富的语义信息，能够表达用户的偏好和商品特征。然而，现有的基于评论的推荐方法要么使用静态词向量模型，要么无法有效提取评论中的长序列特征，导致用户特征表达能力有限。此外，用户与商品之间不同或无用的特征交互对推荐性能的影响被忽略。因此，我们提出了一种基于评论的用户-商品交互注意力分解机（AFMRUI）用于推荐，该方法首先利用RoBERTa获取每个用户/商品评论的嵌入特征，并将双向门控循环单元与注意力网络相结合，以突出用户和商品评论中更有用的信息。然后我们采用注意力分解机（AFM）来学习用户-商品特征交互，以区分不同用户-商品特征交互的重要性，并进一步获得更准确的评分预测，从而促进推荐。最后，我们在五个真实世界数据集上进行了性能评估。五个数据集的实验结果表明，所提出的AFMRUI在两个常用评估指标方面优于基于评论的现有先进方法。
