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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Effect of the Number of Users and Bias of Users’ Preference on Recommender Systems
verfasst von : Akihiro Yamashita, Hidenori Kawamura, Hiroyuki Iizuka, Azuma Ohuchi
Erschienen in: Intelligent Data Engineering and Automated Learning - IDEAL 2007
Verlag: Springer Berlin Heidelberg
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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.