Text Matching Improves Sequential Recommendation by Reducing Popularity Biases

• URL: http://arxiv.org/abs/2308.14029v1
• Date: Sun, 27 Aug 2023 07:44:33 GMT
• Title: Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
• Authors: Zhenghao Liu, Sen Mei, Chenyan Xiong, Xiaohua Li, Shi Yu, Zhiyuan Liu, Yu Gu, Ge Yu
• Abstract summary: TASTE verbalizes items and user-item interactions using identifiers and attributes of items. Our experiments show that TASTE outperforms the state-of-the-art methods on widely used sequential recommendation datasets.
• Score: 48.272381505993366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes Text mAtching based SequenTial rEcommendation model (TASTE), which maps items and users in an embedding space and recommends items by matching their text representations. TASTE verbalizes items and user-item interactions using identifiers and attributes of items. To better characterize user behaviors, TASTE additionally proposes an attention sparsity method, which enables TASTE to model longer user-item interactions by reducing the self-attention computations during encoding. Our experiments show that TASTE outperforms the state-of-the-art methods on widely used sequential recommendation datasets. TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems. Our further analyses illustrate that TASTE significantly improves the recommendation accuracy by reducing the popularity bias of previous item id based recommendation models and returning more appropriate and text-relevant items to satisfy users. All codes are available at https://github.com/OpenMatch/TASTE.

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