Tracking changes in user interests with a few relevance judgments

Authors:
Dwi H. Widyantoro;Thomas R. Ioerger;John Yen
Affiliations:
Texas A&M University, College Station, TX;Texas A&M University, College Station, TX;The Pennsylvania State University, University Park, PA
Venue:
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Year:
2003

Citing 7
Cited 4

Algorithms for clustering data

Algorithms for clustering data
Handbook of logic in artificial intelligence and logic programming (Vol. 4): epistemic and temporal reasoning

Handbook of logic in artificial intelligence and logic programming (Vol. 4): epistemic and temporal reasoning
Learning changing concepts by exploiting the structure of change

COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Improving automatic query expansion

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Real life, real users, and real needs: a study and analysis of user queries on the web

Information Processing and Management: an International Journal
Learning user interest dynamics with a three-descriptor representation

Journal of the American Society for Information Science and Technology
An Incremental Approach to Building a Cluster Hierarchy

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining

Query length impact on misuse detection in information retrieval systems

Proceedings of the 2005 ACM symposium on Applied computing
Building and evaluating a location-based service recommendation system with a preference adjustment mechanism

Expert Systems with Applications: An International Journal
Enhancing recommender systems under volatile userinterest drifts

Proceedings of the 18th ACM conference on Information and knowledge management
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine

ACM Transactions on Intelligent Systems and Technology (TIST)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Keeping track of changes in user interests from a document stream with a few relevance judgments is not an easy task. To tackle this problem, we propose a novel method that integrates (1) pseudo-relevance feedback mechanism, (2) assumption about the persistence of user interests and (3) incremental method for data clustering. This approach has been empirically evaluated using Reuters-21578 corpus in a setting for information filtering. The experiment results reveal that it significantly improves the performances of existing user-interest-tracking systems without requiring additional, actual relevance judgments.