Alexander Kotov
Ryen W. White
Susan T. Dumais
Jaime Teevan
ABSTRACT
The information needs of search engine users vary in complexity, depending on the task they are trying to accomplish. Some simple needs can be satisfied with a single query, whereas others require a series of queries issued over a longer period of time. While search engines effectively satisfy many simple needs, searchers receive little support when their information needs span session boundaries. In this work, we propose methods for modeling and analyzing user search behavior that extends over multiple search sessions. We focus on two problems: (i) given a user query, identify all of the related queries from previous sessions that the same user has issued, and (ii) given a multi-query task for a user, predict whether the user will return to this task in the future. We model both problems within a classification framework that uses features of individual queries and long-term user search behavior at different granularity. Experimental evaluation of the proposed models for both tasks indicates that it is possible to effectively model and analyze cross-session search behavior. Our findings have implications for improving search for complex information needs and designing search engine features to support cross-session search tasks.
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Index Terms
- Modeling and analysis of cross-session search tasks
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Reviews
Kazunari Sugiyama
Current search engines cannot provide acceptable results for complicated needs that require a user to issue a series of queries in multiple search sessions ("cross-session" in this paper), such as when planning a vacation. Kotov et al. model and analyze the cross-session information needs by identifying all previous queries in a user's search history dedicated to the same task as the current query, and by predicting whether a user will return to the task in future sessions. They formalize these tasks as simple supervised classification tasks, and obtain promising findings that knowledge of previous user queries on the same long-term task enables a search engine to provide support for task resumption. To define the task, they employ both automatic initial labeling and additional human annotation. Then, they label the queries as belonging to the same task if the similarity between term sets of two queries exceeds a threshold. Their proposed approach achieved more than 70 percent accuracy. The authors employ only two different regression-based classifiers. However, to verify which classifier is effective in this type of task, they also should try other popular classifiers such as support vector machines, maximum entropy, and so on. They analyze important features to construct classifiers obtained by logistic regression in detail. Their findings are helpful cues for researchers who work on user search behavior. Online Computing Reviews Service
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