A Case-Based Recommender System Using Implicit Rating Techniques

Authors:
Youngji Kim;Sooho Ok;Yongtae Woo
Affiliations:
-;-;-
Venue:
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Year:
2002

Citing 4
Cited 4

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Abstract

We propose a new case-based recommender system using implicit rating information. We present intra-attribute and inter-attribute weight derived from past interests of a user stored in the access logs, and a new similarity function to estimate similarities between new items set and the user profile. To verify the efficiency of our system, we have performed experimental comparisons between the proposed model and the collaborative filtering technique by mean absolute error(MAE) and receiver operating characteristic(ROC). The results show that the proposed model is more efficient than the traditional collaborative filtering technique.