Abstract
Query term deletion is one of the commonly used strategies for query rewriting. In this paper, we study the problem of query term deletion using large-scale e-commerce search logs. Specifically, we focus on queries that do not lead to user clicks and aim to predict a reduced and better query that can lead to clicks by term deletion. Accurate prediction of term deletion can potentially help users recover from poor search results and improve shopping experience. To achieve this, we use various term-dependent and query-dependent measures as features and build a classifier to predict which term is the most likely to be deleted from a given query. Our approach is data-driven. We investigate the large-scale query history and the document collection, verify the usefulness of previously proposed features, and also propose to incorporate the query category information into the term deletion predictors. We observe that training within-category classifiers can result in much better performance than training a unified classifier. We validate our approach using a large collection of query sessions logs from a leading e-commerce site and demonstrate that our approach provides promising performance in query term deletion prediction.
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Yang, B., Parikh, N., Singh, G., Sundaresan, N. (2014). A Study of Query Term Deletion Using Large-Scale E-commerce Search Logs. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_20
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DOI: https://doi.org/10.1007/978-3-319-06028-6_20
Publisher Name: Springer, Cham
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