构建跨数据集实体匹配基准，助力推荐系统精准关联商品信息。
Evaluation on Entity Matching in Recommender Systems
- 新数据集融合Reddit与Amazon商品，手工标注跨平台对应关系。
- 评测多种匹配方法，发现大模型在跨域匹配中表现最优。
- 开源标注数据和映射结果，支持可复现研究与下游应用。
实体匹配是对话推荐系统（CRS）和基于知识的推荐系统中的关键环节。然而，缺乏严格的跨数据集实体匹配评估框架，制约了大模型驱动的对话推荐及知识基础数据集构建的发展。本文提出Reddit-Amazon-EM，一个由Reddit和Amazon '23数据集中的自然商品组成的新型数据集。通过人工精心标注，我们识别出Reddit-Movies与Amazon'23中对应的电影条目，这两个数据集本就存在潜在重叠。基于该数据集，我们对当前主流的规则、图、词法、嵌入和大模型基实体匹配方法进行了全面评估。为确保研究可复现性，我们公开了手动标注的黄金标准匹配集，并以实验中表现最佳的方法提供两数据集间的映射关系。该资源将推动推荐系统中实体匹配的进一步研究。数据与代码可在 https://github.com/huang-zihan/Reddit-Amazon-Entity-Matching 获取。
原文摘要 · Abstract (English)
Entity matching is a crucial component in various recommender systems, including conversational recommender systems (CRS) and knowledge-based recommender systems. However, the lack of rigorous evaluation frameworks for cross-dataset entity matching impedes progress in areas such as LLM-driven conversational recommendations and knowledge-grounded dataset construction. In this paper, we introduce Reddit-Amazon-EM, a novel dataset comprising naturally occurring items from Reddit and the Amazon '23 dataset. Through careful manual annotation, we identify corresponding movies across Reddit-Movies and Amazon'23, two existing recommender system datasets with inherently overlapping catalogs. Leveraging Reddit-Amazon-EM, we conduct a comprehensive evaluation of state-of-the-art entity matching methods, including rule-based, graph-based, lexical-based, embedding-based, and LLM-based approaches. For reproducible research, we release our manually annotated entity matching gold set and provide the mapping between the two datasets using the best-performing method from our experiments. This serves as a valuable resource for advancing future work on entity matching in recommender systems.Data and Code are accessible at: https://github.com/huang-zihan/Reddit-Amazon-Entity-Matching.
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