REDGCN: Rating-Oriented Explicit Disentangling Graph Convolution Network for Review-Aware Recommendation

Sheng Sang, Feng Xue, Hao Guo, Kang Liu, Shuaiyang Li, Richang Hong

Abstract: Rating prediction is a challenging task in review-aware recommendation. Although current methods effectively combine collaborative signals with review data, they fail to differentiate user preferences across various ratings and overlook the independence between these ratings. In this article, we emphasize the importance of independence modeling among representations for different rating levels. To this end, we propose a rating-oriented explicit disentangling graph convolution network for review-aware recommendation, short for REDGCN. Specifically, we introduce a rating-oriented disentangled representation learning that segments representations and rating graph based on ratings. It also employs an explicit graph learning approach to ensure the independence of disentangled representations during information propagation, which mitigates noise from review features. Furthermore, we define and model one kind of cross-rating correlation, based on the characteristics of user rating behavior. By leveraging this approach, we introduce a cross-rating constraint as an additional task to further enhance the independence among disentangled representations and improve the stability of model training. We conduct extensive experiments on six public datasets to prove the effectiveness of REDGCN. The complete data and codes of REDGCN are available at https://github.com/hfutmars/REDGCN.

External IDs:dblp:journals/tcss/SangXGLLH25