NDLI: To personalize or not to personalize: modeling queries with variation in user intent

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To personalize or not to personalize: modeling queries with variation in user intent

Content Provider	ACM Digital Library
Author	Liebling, Daniel J. Teevan, Jaime Dumais, Susan T.
Abstract	In most previous work on personalized search algorithms, the results for all queries are personalized in the same manner. However, as we show in this paper, there is a lot of variation across queries in the benefits that can be achieved through personalization. For some queries, everyone who issues the query is looking for the same thing. For other queries, different people want very different results even though they express their need in the same way. We examine variability in user intent using both explicit relevance judgments and large-scale log analysis of user behavior patterns. While variation in user behavior is correlated with variation in explicit relevance judgments the same query, there are many other factors, such as result entropy, result quality, and task that can also affect the variation in behavior. We characterize queries using a variety of features of the query, the results returned for the query, and people's interaction history with the query. Using these features we build predictive models to identify queries that can benefit from personalization.
Starting Page	163
Ending Page	170
Page Count	8
File Format	PDF
ISBN	9781605581644
DOI	10.1145/1390334.1390364
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Potential for personalization Personalized search Clarity
Content Type	Text
Resource Type	Article

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