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. The explanation generated by existing works are too general rather than focusing on certain topics or specific item features, 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 the pseudo user and item profile 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 outperforms the state-of-the-art in generation.