MARS通过属性感知编码与匹配，提升文本序列推荐精度
MARS: Matching Attribute-aware Representations for Text-based Sequential Recommendation
- 用属性感知编码提取用户和物品的多维度表示
- 在属性层面进行交互匹配，精准捕捉用户偏好
- 在5个数据集上召回率最高提升29.26%，适合做推荐系统研究者参考
序列推荐旨在基于用户的交互历史预测其可能偏好的下一个物品。近年来，基于文本的序列推荐成为有前景的范式，利用预训练语言模型挖掘文本物品特征以提升性能，并促进知识迁移至未见数据集。然而，现有基于文本的推荐模型仍面临两大挑战：(i) 对具有多种属性的用户和物品进行建模；(ii) 匹配具有复杂兴趣的用户与物品。为此，本文提出一种新模型——匹配属性感知表示的文本序列推荐（MARS）。MARS通过属性感知文本编码提取详细的用户与物品表示，捕捉多样化的用户意图。随后，通过属性级交互匹配计算用户-物品评分，有效捕获属性层面的用户偏好。大量实验表明，MARS显著优于现有序列推荐模型，在五个基准数据集上，Recall@10最高提升24.43%，NDCG@10最高提升29.26%。代码已开源。
原文摘要 · Abstract (English)
Sequential recommendation aims to predict the next item a user is likely to prefer based on their sequential interaction history. Recently, text-based sequential recommendation has emerged as a promising paradigm that uses pre-trained language models to exploit textual item features to enhance performance and facilitate knowledge transfer to unseen datasets. However, existing text-based recommender models still struggle with two key challenges: (i) representing users and items with multiple attributes, and (ii) matching items with complex user interests. To address these challenges, we propose a novel model, Matching Attribute-aware Representations for Text-based Sequential Recommendation (MARS). MARS extracts detailed user and item representations through attribute-aware text encoding, capturing diverse user intents with multiple attribute-aware representations. It then computes user-item scores via attribute-wise interaction matching, effectively capturing attribute-level user preferences. Our extensive experiments demonstrate that MARS significantly outperforms existing sequential models, achieving improvements of up to 24.43% and 29.26% in Recall@10 and NDCG@10 across five benchmark datasets. Code is available at https://github.com/junieberry/MARS
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