BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Recommender systems for evaluating computer messages
Communications of the ACM
Query refinement for multimedia similarity retrieval in MARS
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
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
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
QCluster: relevance feedback using adaptive clustering for content-based image retrieval
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Cluster Analysis
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As new items are frequently released nowadays, item providers and customers need the recommender system which is specialized in recommending new items. Because most of previous approaches for recommender system have to rely on the usage history of customers, collaborative filtering is not directly applicable to solve the new item problem. Therefore they have suggested content-based recommender system using feature values of new items. However it is not sufficient to recommend new items. This research aims to suggest hybrid recommendation procedures based on preference boundary of target customer. We suggest TC, BC, and NC algorithms to determine the preference boundary. TC is an algorithm developed from contents-based filtering, whereas BC and NC are algorithms based on collaborative filtering, which incorporates neighbors, similar customers to a target customer. We evaluate the performances of suggested algorithms with real mobile image transaction data set. Experimental test results that the performances of BC and NC is better than that of TC, which means that the suggested hybrid procedures are more effective than the content-based approach.