Communications of the ACM
Modeling user interest shift using a Bayesian approach
Journal of the American Society for Information Science and Technology
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Adaptive interfaces for ubiquitous web access
Communications of the ACM - The Adaptive Web
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking changes in user interests with a few relevance judgments
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic Content-Based Recommendation in e-Commerce
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Feature weighting in content based recommendation system using social network analysis
Proceedings of the 17th international conference on World Wide Web
Detection of the customer time-variant pattern for improving recommender systems
Expert Systems with Applications: An International Journal
Intelligent product search with soft-boundary preference relaxation
Expert Systems with Applications: An International Journal
Product recommendation with temporal dynamics
Expert Systems with Applications: An International Journal
Using program synthesis for social recommendations
Proceedings of the 21st ACM international conference on Information and knowledge management
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
| Hi-index | 0.01 |
This paper presents a systematic study of how to enhance recommender systems under volatile user interest drifts. A key development challenge along this line is how to track user interests dynamically. To this end, we first define four types of interest patterns to understand users' rating behaviors and analyze the properties of these patterns. We also propose a rating graph and rating chain based approach for detecting these interest patterns. For each users' rating series, a rating graph and a rating chain are constructed based on the similarities between rated items. The type of a given user's interest pattern is identified through the density of the corresponding rating graph and the continuity of the corresponding rating chain. In addition, we propose a general algorithm framework for improving recommender systems by exploiting these identified patterns. Finally, experimental results on a real-world data set show that the proposed rating graph based approach is effective for detecting user interest patterns, which in turn help to improve the performance of recommender systems.