Applying collaborative filtering for efficient document search

Seikyung Jung, Juntae Kim, Jonathan L. Herlocker

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

This paper presents the SERF (System for Electronic Recommendation Filtering) which is a collaborative filtering system that recommends context-sensitive, high-quality information sources for document search. Collaborative filtering systems remove the limitation of traditional content-based search by using individual's ratings to evaluate and recommend information sources. SERF uses collaborative filtering algorithms to predict the relevance and quality of each document with respect to each particular user and their specific information need. In our system, users specify their need in the form of a natural language query, and are provided with recommended documents based on ratings by other users with similar questions. Preliminary experiments show that the collaborative filtering recommendations increase the efficiency of the document search process. We also discuss some key challenges of designing a collaborative filtering system for document search.

Original languageEnglish
Title of host publicationProceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004
EditorsN. Zhong, H. Tirri, Y. Yao, L. Zhou
Pages640-643
Number of pages4
StatePublished - 2004
EventProceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004 - Beijing, China
Duration: 20 Sep 200424 Sep 2004

Publication series

NameProceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004

Conference

ConferenceProceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004
Country/TerritoryChina
CityBeijing
Period20/09/0424/09/04

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