Abstract
It is well-known that for a variety of search tasks involving queries more relevant results can be presented if they are personalized according to a user’s interests and search behavior. This can be achieved with user-dependent, rich web search queries profiles. These are typically built as part of a specific search personalization task so that it is unclear which characteristics of queries are most effective for modeling the user-query relationship in general. In this paper, we explore various approaches for explicitly modeling this user-query relationship independently of other search components. Our models employ generative models in layers in a prediction task. The results show that the best signals for modeling the user-query relationship come from the given query’s terms and entities together with information from related entities and terms, yielding a relative improvement of up to 24.5% in MRR and Success over the baseline methods.
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Aktolga, E., Jain, A., Velipasaoglu, E. (2013). Building Rich User Search Queries Profiles. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_21
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DOI: https://doi.org/10.1007/978-3-642-38844-6_21
Publisher Name: Springer, Berlin, Heidelberg
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