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.

该研究提出了一种基于文本匹配的顺序推荐模型（TASTE），通过将物品和用户映射到一个嵌入空间，并采用匹配文本表示来推荐物品。TASTE通过使用物品的标识符和属性来表达物品和用户-物品交互，提出了一个关注稀疏性方法，通过减少编码过程中的自注意计算，使TASTE能够更好地刻画用户行为。实验证明，TASTE在广泛使用的顺序推荐数据集上优于现有方法。TASTE通过使用全文建模来表示长尾物品，并将预训练语言模型的优势引入推荐系统，从而缓解了冷启动问题。进一步的分析表明，TASTE通过减少先前基于物品ID的推荐模型的流行度偏见，返回更加适当和与文本相关的物品以满足用户，显著提高了推荐准确性。所有代码都可以在该https URL找到。

文本匹配通过减少流行偏见，改善顺序推荐