Personalized query suggestion based on user behavior

Wanyu Chen, Zepeng Hao (), Taihua Shao () and Honghui Chen ()
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Wanyu Chen: Science and Technology on Information Systems, Engineering Laboratory, National University of Defense Technology, Hunan 410073, P. R. China
Zepeng Hao: Science and Technology on Information Systems, Engineering Laboratory, National University of Defense Technology, Hunan 410073, P. R. China
Taihua Shao: Science and Technology on Information Systems, Engineering Laboratory, National University of Defense Technology, Hunan 410073, P. R. China
Honghui Chen: Science and Technology on Information Systems, Engineering Laboratory, National University of Defense Technology, Hunan 410073, P. R. China

International Journal of Modern Physics C (IJMPC), 2018, vol. 29, issue 04, 1-15

Abstract: Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantical similarity and co-occurrence which indicates the behavior information from other users in web search. Regarding the current user’s preference to a query, we combine the user’s short-term and long-term search behavior in a linear fashion and deal with the data sparse problem with Bayesian probabilistic matrix factorization (BPMF). In particular, we also investigate the impact of different personalization strategies (the combination of the user’s short-term and long-term search behavior) on the performance of query suggestion reranking. We quantify the improvement of our proposed UB model against a state-of-the-art baseline using the public AOL query logs and show that it beats the baseline in terms of metrics used in query suggestion reranking. The experimental results show that: (i) for personalized ranking, users’ behavioral information helps to improve query suggestion effectiveness; and (ii) given a query, merging information inferred from the short-term and long-term search behavior of a particular user can result in a better performance than both plain approaches.

Keywords: Personalization; user behavior; Bayesian probabilistic matrix factorization; query suggestion (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0129183118500365

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