Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation. Traditional sequential recommendation models usually utilize ID embeddings to represent items, which are not shared across different domains and lack the transferable ability. Recent studies use pre-trained language models (PLM) for item text embeddings (text-based IRL) that are universally applicable across domains. However, the existing text-based IRL is unaware of the important collaborative filtering (CF) information. In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation. To effectively incorporate CF information into text-based IRL, we convert the item-level interaction data to a word graph containing word-level collaborations. Subsequently, we design a novel pre-training task to align the word-level semantic- and CF-related item representation. Extensive experimental results on multiple public datasets demonstrate that compared to state-of-the-art transferable sequential recommenders, CoWPiRec achieves significantly better performances in both fine-tuning and zero-shot settings for cross-scenario recommendation and effectively alleviates the cold-start issue. The code is available at: https://github.com/ysh-1998/CoWPiRec.

翻译：物品表示学习在推荐系统中至关重要，尤其对于序列推荐。传统序列推荐模型通常使用ID嵌入表示物品，这类表示无法跨领域共享且缺乏可迁移能力。近期研究利用预训练语言模型获得物品文本嵌入（基于文本的物品表示），这类表示可跨领域通用。然而，现有基于文本的物品表示未能捕捉重要的协同过滤信息。本文提出CoWPiRec——一种面向推荐的协作式基于词的预训练物品表示方法。为有效将协同过滤信息融入基于文本的物品表示，我们将物品级交互数据转换为包含词级协作关系的词图，进而设计新颖的预训练任务，对齐词级语义相关与协同过滤相关的物品表示。在多个公开数据集上的大量实验结果表明，与最先进的可迁移序列推荐方法相比，CoWPiRec在跨场景推荐的微调与零样本设置下均显著提升性能，并有效缓解冷启动问题。代码开源地址：https://github.com/ysh-1998/CoWPiRec。