A Probabilistic Approach for Mining Drifting User Interest

Authors:
Pin Zhang;Juhua Pu;Yongli Liu;Zhang Xiong
Affiliations:
School of Computer Science and Technology, Beihang University, Beijing, China 100083;School of Computer Science and Technology, Beihang University, Beijing, China 100083;School of Computer Science and Technology, Beihang University, Beijing, China 100083;School of Computer Science and Technology, Beihang University, Beijing, China 100083
Venue:
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Year:
2009

Citing 10
Cited 0

Learning in the presence of concept drift and hidden contexts

Machine Learning
Incremental relevance feedback for information filtering

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A hybrid user model for news story classification

UM '99 Proceedings of the seventh international conference on User modeling
Learning user interest dynamics with a three-descriptor representation

Journal of the American Society for Information Science and Technology
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Collaborative filtering via gaussian probabilistic latent semantic analysis

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Bayesian Networks Structure Learning and Its Application to Personalized Recommendation in a B2C Portal

WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
A Community-Based Recommendation System to Reveal Unexpected Interests

MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Efficient distribution-free learning of probabilistic concepts

SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

Incremental approaches learn drifting user interests mainly from user feedbacks. Most of those existing approaches assume that data instances in user feedbacks are binary labeled. This paper presents a novel probabilistic approach that learns drifting user interests from numerically labeled feedbacks instead of binary labeled ones. The approach models user interests as a set of probabilistic concepts, considers numerical instance labels as probabilities that the user likes those instances, and uses feedbacks to update user interest models incrementally based on an exponential, recency-weighted average algorithm. Experimental results on different learning tasks show that the approach outperforms existing approaches in numerically labeled feedback environment.