Paper: An Iterative Implicit Feedback Approach To Personalized Search

ACL ID P06-1074
Title An Iterative Implicit Feedback Approach To Personalized Search
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

General information retrieval systems are designed to serve all users without con- sidering individual needs. In this paper, we propose a novel approach to person- alized search. It can, in a unified way, exploit and utilize implicit feedback in- formation, such as query logs and imme- diately viewed documents. Moreover, our approach can implement result re-ranking and query expansion simultaneously and collaboratively. Based on this approach, we develop a client-side personalized web search agent PAIR (Personalized Assis- tant for Information Retrieval), which supports both English and Chinese. Our experiments on TREC and HTRDP col- lections clearly show that the new ap- proach is both effective and efficient.