GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
A Personalized Restaurant Recommender Agent for Mobile E-Service
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
IEEE Transactions on Knowledge and Data Engineering
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Recommender System provides certain products adapted to a target user, from a large number of products. One of the most successful recommendation algorithms is Collaborative Filtering, and it is used in many websites. However, the recommendation result is influenced by community characteristics such as the number of users and bias of users' preference, because the system uses ratings of products by the users at the recommendation. In this paper, we evaluate an effect of community characteristics on recommender system, using multi-agent based simulation. The results show that a certain number of ratings are necessary to effective recommendation based on collaborative filtering. Moreover, the results also indicate that the number of necessary ratings for recommendation depends on the number of users and bias of the users' preference.