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Deriving Concept-based User Profiles from Search Engine Logs
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| Citations: | 6 - 0 self |
BibTeX
@MISC{Leung_derivingconcept-based,
author = {Kenneth Wai-ting Leung and Dik Lun Lee},
title = {Deriving Concept-based User Profiles from Search Engine Logs},
year = {}
}
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Abstract
Abstract—User profiling is a fundamental component of any personalization applications. Most existing user profiling strategies are based on objects that users are interested in (i.e. positive preferences), but not the objects that users dislike (i.e. negative preferences). In this paper, we focus on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences. We evaluate the proposed methods against our previously proposed personalized query clustering method. Experimental results show that profiles which capture and utilize both of the user’s positive and negative preferences perform the best. An important result from the experiments is that profiles with negative preferences can increase the separation between similar and dissimilar queries. The separation provides a clear threshold for an agglomerative clustering algorithm to terminate and improve the overall quality of the resulting query clusters. Index Terms—Negative preferences, personalization, personalized query clustering, search engine, user profiling. 1
Keyphrases
negative preference search engine log concept-based user profile agglomerative clustering algorithm positive preference fundamental component important result user profiling strategy overall quality query clustering user profiling index term negative preference several concept-based user clear threshold abstract user profiling search engine personalization application experimental result query cluster search engine personalization dissimilar query
