|关键词: 特征交互 评论推荐 多粒度 卷积神经网络
|Recommendation model based on fine-grained feature interaction
|YANG Zhenyu,LIU Guojing,WANG Yu
|School of lnformation and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
|With the rise of e-commerce platforms, users left a large number of reviews while shopping online. These reviews usually contain rich semantic information about user interests and item attributes, reflecting user preferences for item features. In recent years, many deep learning-based solutions have been proposed by exploiting reviews for recommendation, and have achieved great success. The attention mechanism is mainly adopted in these works to identify words or aspects that are important for rating prediction. They singularly extract feature information from reviews, and obtain prediction ratings through the feature interaction between users and items. However, excessive aggregation may result in the loss of fine-grained information in reviews. In addition, the existing models either ignore the relevance of user and item reviews or construct review feature interactions only at a single granularity, which results in the features information of users and items not being captured efficiently and comprehensively. In order to address the above issue, in this paper we consider that by capturing feature information from multiple granularities of each review, and then performing feature interactions at multiple granularities for users and items could enable a better rating prediction with explainability.To this end, we propose a novel fine-grained feature interaction network (FFIN) for rating prediction in this paper. In the first place, instead of aggregating all the user's reviews into a uniform vector, the model deals with each review for the user and the item separately, and construct a multi-level representation for each review text hierarchically by stacked extended convolution, which adequately captures the multi-granularity semantic information of the review. In the second place, the model constructs the fine-grained feature interaction of user and item reviews at each semantic level, which effectively avoids the problem that secondary important information is overlooked due to single-grained interaction. In the end, the user's review is usually subjective and personalized, instead of using the attention mechanism to identify important informations, the high-order salient signals are identified by a hierarchical structure similar to image recognition and used in the final rating prediction. Extensive experiments are conducted over six real-world datasets with diverse characteristics. Our results demonstrate that the proposed FINN obtains substantial performance gain over recently proposed state-of-the-art models in terms of prediction accuracy. At the same time, further experimental analysis shows that the interaction of multi-granularity features not only highlights the relevant information in the reviews, but also greatly improves the interpretability of the rating prediction.
|Key words: feature interaction review recommendation multi-granularity convolutional neural network