On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A hidden Markov model information retrieval system
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Simple Semantics in Topic Detection and Tracking
Information Retrieval
Enhancing topic tracking with temporal information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Dependency structure language model for topic detection and tracking
Information Processing and Management: an International Journal
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Recommendation in Internet forums and blogs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
User comments for news recommendation in forum-based social media
Information Sciences: an International Journal
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In web recommender systems, clustering is done offline to extract usage patterns and a successful recommendation highly depends on the quality of this clustering solution. As for collaborative recommendation, there are two ways to calculate the similarity for clique recommendation: Item-based Clustering Method and User-based Clustering Method. Researches have proved that item-based collaborative filtering is better than user-based collaborative filtering at precision and computation complexity. However, the common item-based clustering technologies could not quite suit for news recommender system, since the news events evolve fast and continuous. In this paper, we suggest using technologies of TDT to group news items instead of common item-based clustering technologies. Experimental results are examined that shows the usefulness of our approach.