TY - GEN
T1 - Applying collaborative filtering for efficient document search
AU - Jung, Seikyung
AU - Kim, Juntae
AU - Herlocker, Jonathan L.
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=15544385773&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:15544385773
SN - 0769521002
T3 - Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004
SP - 640
EP - 643
BT - Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004
A2 - Zhong, N.
A2 - Tirri, H.
A2 - Yao, Y.
A2 - Zhou, L.
T2 - Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004
Y2 - 20 September 2004 through 24 September 2004
ER -