2011 UserClickModelingforUnderstandi

(Zhang, Chen et al., 2011) ⇒ Yuchen Zhang, Weizhu Chen, Dong Wang, and Qiang Yang. (2011). “User-click Modeling for Understanding and Predicting Search-behavior.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020613

Subject Headings:

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Author Keywords

Algorithms; click log analysis; experimentation; information search and retrieval; performance; task-centric click model

Abstract

Recent advances in search users' click modeling consider both users' search queries and click / skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session.

Investigating this problem, we consider the sequence of queries and their clicks in a search session as a task and propose a task-centric click model (TCM). TCM characterizes user behavior related to a task as a collective whole. Specifically, we identify and consider two new biases in TCM as the basis for user modeling. The first indicates that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer. The other illustrates that users tend to click fresh documents that are not included in the results of previous queries. Using these biases, TCM is more accurately able to capture user search behavior. Extensive experimental results demonstrate that by considering all the task information collectively, TCM can better interpret user click behavior and achieve significant improvements in terms of ranking metrics of NDCG and perplexity.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 UserClickModelingforUnderstandi	Qiang Yang Yuchen Zhang Weizhu Chen Dong Wang			User-click Modeling for Understanding and Predicting Search-behavior				10.1145/2020408.2020613		2011