  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.
