Fusing Knowledge and Aspect Sentiment for Explainable Recommendation

Abstract

The current research of recommender system mainly focuses on improving the accuracy of the recommendation, but pays less attention to explainability. Explainability is essential to enhance users’ trust and satisfaction, which can even increase the likelihood of buying items. In the existing explainable recommender systems, the mining of explicit and implicit features is not comprehensive, and the interaction among these features is not considered plentifully. In addition, the generated recommended reason text is not personalized and rich enough. It is necessary to improve the quality of recommended reason text because it is difficult to meet the needs of different users by using low-quality text. In this paper, we propose a new method that fuses external knowledge and aspect sentiment to predict rating and generate personalized, content-rich recommended reasons, which applies fine-tuning BERT to solve aspect-based sentiment analysis and extends Transformer to generate recommended reason by using bi-directional attention. The experiment results on real-world datasets demonstrated that our method was effective, and our model was superior to the baseline models on various metrics. For rating prediction task, our model can achieve an improvement of 0.6% on average in terms of RMSE. For recommended reason generation task, our model can achieve an improvement of 9.2% to 11.3% over state-of-the-arts in terms of BLEU.