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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.

목차

ABSTRACT
 1. INTRODUCTION
 2. RESEARCH BACKGROUD
  2.1 Recommender Systems for New Items
  2.2 Representation of Preference Boundary
  2.3 Neighbor Formation
 3. METHODOLOGY
  3.1 Overall Procedure
  3.2 Preference Boundary By TargetCustomer (TC)
  3.3 Preference Boundary By Big TargetCustomer (BC)
  3.4 Preference Boundary By TargetCustomer With Neighbors (NC)
  3.5 Determination of Preference BoundaryRange
 4. EXPERIMENTAL EVALUATION
  4.1 Data Set
  4.2 Experimental Environment
  4.3 Experimental Result
 5. Conclusion
 6. ACKNOWLEDGMENTS
 7. REFERENCES

저자

• Jae Kyeong Kim [ School of Management, Kyung Hee University ]
• Moon Kyoung Jang [ School of Management, Kyung Hee University ]
• Hyea Kyeong Kim [ School of Management, Kyung Hee University ]
• Yoon Ho Cho [ School of E-Business, KookMin University ]

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