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ABSTRACT

Query classification (QC) has been widely studied to understand users’ search intent. For e-commerce search queries, users typically search for either a specific product or a category of products. In both cases, a query can be associated with a category label that belongs to a taxonomy tree describing the
items in the catalog. However, product-related search queries aren typically short, ambiguous, and continuously changing depending on seasonal trends and the introduction of new products over time. Traditional supervised approaches to e-commerce QC are not feasible due to the high cost of manual annotation and the high volume of traffic on e-commerce search engines. In this work, we introduce an unsupervised method to collect large amounts of query classification data using user’s implicit click feedback. We obtain a large multi-label dataset containing 403,349 unique queries from 2,085 categories. We compare and contrast different state-of-the-art text classifiers and demonstrate
that an ensemble of linear SVMs models achieves a micro-F1 score of 0.60 and 0.82 at leaf and top level, respectively.