Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
User Modeling and User-Adapted Interaction
User preference modeling from positive contents for personalized recommendation
DS'07 Proceedings of the 10th international conference on Discovery science
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Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of contents that are likely to fit their needs. One notable challenge in a recommender system is the cold start problem. To address this issue, we propose a collaborative approach to user modeling for generating personalized recommendations for users. Our approach first discovers useful and meaningful patterns of users, and then enriches a personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user-based collaborative filtering, and vector space model. We present experimental results that show how our model performs better than existing work.