Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search
Improving the quality of search results can significantly enhance users
experience and engagement with search engines. In spite of several recent
advancements in the fields of machine learning and data mining, correctly
classifying items for a particular user search query has been a long-standing
challenge, which still has a large room for improvement. This paper introduces
the "Shopping Queries Dataset", a large dataset of difficult Amazon search
queries and results, publicly released with the aim of fostering research in
improving the quality of search results. The dataset contains around 130
thousand unique queries and 2.6 million manually labeled (query,product)
relevance judgements. The dataset is multilingual with queries in English,
Japanese, and Spanish. The Shopping Queries Dataset is being used in one of the
KDDCup'22 challenges. In this paper, we describe the dataset and present three
evaluation tasks along with baseline results: (i) ranking the results list,
(ii) classifying product results into relevance categories, and (iii)
identifying substitute products for a given query. We anticipate that this data
will become the gold standard for future research in the topic of product
search.