RAKCR: Reviews sentiment-aware based knowledge graph convolutional networks for Personalized Recommendation

The recommendation algorithm is an important means to alleviate the information explosion in the era of big data. There has been a great deal of research into the use of knowledge graphs as auxiliary information in recommender systems, which can be used to alleviate data sparsity and cold start problems. However, most knowledge graph-based recommendation methods only use rating data to capture the user's potential interest, and the rating is only a comprehensive evaluation of the item by the user, which cannot intuitively and accurately express the user's personalized preference. In addition, existing recommendation strategies that blend ratings and reviews cannot simultaneously model the aspect fine-grained sentiment preferences of users in reviews as well as the personalized characteristics of items from the user's perspective. To this end, in this paper, we propose Reviews Sentiment-Aware Knowledge Graph Convolutional Neural Network (RAKCR), a generic review and knowledge graph-based framework that provides better recommendations by fully mining the fine-grained personalization features in user reviews. In contrast to existing correlation recommendation methods, we designed a new reviews sentiment perception feature and knowledge graph alignment module to characterize user preferences for specific features of items in the knowledge graph. To better represent the personalized feature distribution of users and items, we use the proposed RAKCR to aggregate sentiment relationship weight-aware neighborhood information in the knowledge graph to capture personalized feature representations of both users and items, and to better learn user and item embeddings for more accurate personalized recommendations. Experimental results demonstrate that the proposed RAKCR model outperforms the benchmark model significantly in click-through rate prediction for recommendation scenarios. Across the three datasets, Movielens-20 m, Amazon-book, and Yelp, the AUC values show an average improvement of 6.4%, 6.0%, and 3.4%, respectively. Additionally, the F1 values exhibit an average improvement of 7.2%, 6.2%, and 4.1%, respectively, when compared to existing state-of-the-art methods.