Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products. We argue that evaluating with such a dataset may yield unreliable results and conclusions, and deviate from real user satisfaction. To overcome these problems, in this paper, we release a personalized product search dataset comprised of real user queries and diverse user-product interaction types (clicking, adding to cart, following, and purchasing) collected from JD.com, a popular Chinese online shopping platform. More specifically, we sample about 170,000 active users on a specific date, then record all their interacted products and issued queries in one year, without removing any tail users and products. This finally results in roughly 12,000,000 products, 9,400,000 real searches, and 26,000,000 user-product interactions. We study the characteristics of this dataset from various perspectives and evaluate representative personalization models to verify its feasibility. The dataset can be publicly accessed at Github: https://github.com/rucliujn/JDsearch.

翻译：近年来，个性化商品搜索引起了广泛关注，并涌现出多种模型。为评估这些模型的有效性，现有研究主要使用模拟的亚马逊推荐数据集，该数据集包含自动生成的查询，并排除了冷门用户和尾产品。我们认为，使用此类数据集进行评估可能会产生不可靠的结果和结论，偏离真实用户满意度。为解决这些问题，本文发布了一个基于中国热门电商平台京东（JD.com）的个性化商品搜索数据集，该数据集包含真实用户查询及多种用户-商品交互类型（点击、加入购物车、关注和购买）。具体而言，我们在特定日期采样了约17万名活跃用户，并记录他们在一年内交互的所有商品及发出的查询，未移除任何尾用户和尾产品。最终数据集包含约1200万件商品、940万次真实搜索以及2600万次用户-商品交互。我们从多个角度研究了该数据集的特征，并评估了代表性个性化模型以验证其可行性。该数据集可通过GitHub公开访问：https://github.com/rucliujn/JDsearch。