Towards Explainable Recommendation Via Bert-Guided Explanation Generator

Explainable recommender system has recently drawn increasing attention due to its capability of providing justification to recommendation. Rather than focusing on certain topics or specific item features, the explanation generated by existing works are too general without the guidance of aspects. However, such information is not given in the practical scenario. To address this issue, we propose a novel Explainable recommender system with BERT-guided explanation generator, named ExBERT to generate reliable explanation with finer granularity. More specifically, a multi-head self-attention based encoder is employed to incorporate pseudo user and item profiles into semantic representation. Moreover, we propose a novel matched explanation prediction task with discriminative ability to enable personalization of the generated sentence. Extensive experiments conducted on two real-world explainable recommendation datasets significantly outperform the state-of-the-art in generation.

1. INTRODUCTION

Recommendation system has become essential to alleviate the problem of information explosion and considerable research efforts are devoted to enhance the recommendation performance, ranging from collaborative filtering [1], latent factor model [2], [3] to deep neural network [4], [5] and graph neural network [6], [7], [8], etc. However, recommender system still remains a black-box. To increase its persuasiveness, explainable recommendation is an emerging topic which sheds light on the uncertainties with convincing explanation.