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Interest-based personalized search
| Venue: | ACM Trans. Inf. Syst |
| Citations: | 35 - 0 self |
BibTeX
@ARTICLE{Ma_interest-basedpersonalized,
author = {Zhongming Ma and Gautam Pant and Olivia R. Liu Sheng},
title = {Interest-based personalized search},
journal = {ACM Trans. Inf. Syst},
year = {},
volume = {25},
pages = {2007}
}
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Abstract
Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically maps a set of known user interests onto a group of categories in the Open Directory Project (ODP) and takes advantage of manually edited data available in ODP for training text classifiers that correspond to, and therefore categorize and personalize search results according to user interests. In two sets of controlled experiments, we compare our personalized categorization system (PCAT) with a list interface system (LIST) that mimics a typical search engine and with a nonpersonalized categorization system (CAT). In both experiments, we analyze system performances on the basis of the type of task and query length. We find that PCAT is preferable to LIST for information gathering types of tasks and for searches with short queries, and PCAT outperforms CAT in both information gathering and finding types of tasks, and for searches associated with free-form queries. From the subjects ’ answers to a questionnaire, we find that PCAT is perceived as a system that can find relevant Web pages quicker and easier
Keyphrases
user interest search result information gathering free-form query query length information gathering type open directory project categorization system system performance personalized search approach web search engine nonpersonalized categorization system mapping framework subject answer client side controlled experiment relevant web page quicker text classifier list interface system conventional search engine pcat outperforms cat typical search engine short query
