Personalized Transformer for Explainable Recommendation

Lei Li, Yongfeng Zhang, Li Chen

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

55 Citations (Scopus)


Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item IDs are important identifiers for personalization. Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words. To address this problem, we present a PErsonalized Transformer for Explainable Recommendation (PETER), on which we design a simple and effective learning objective that utilizes the IDs to predict the words in the target explanation, so as to endow the IDs with linguistic meanings and to achieve personalized Transformer. Besides generating explanations, PETER can also make recommendations, which makes it a unified model for the whole recommendation-explanation pipeline. Extensive experiments show that our small unpretrained model outperforms fine-tuned BERT on the generation task, in terms of both effectiveness and efficiency, which highlights the importance and the nice utility of our design.
Original languageEnglish
Title of host publicationProceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
PublisherAssociation for Computational Linguistics (ACL)
Number of pages11
Publication statusPublished - Aug 2021
Event59th Annual Meeting of the Association for Computational Linguistics, ACL 2021 - Bangkok, Thailand
Duration: 1 Aug 20216 Aug 2021


Conference59th Annual Meeting of the Association for Computational Linguistics, ACL 2021


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