Abstract
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.
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References
Resnick, P., Varian, H.: Recommender Systems. Comm. of the ACM 40(3), 56–89 (1997)
Reategui, E., Campbell, J.A., Torres, R.: Using Item Descriptors in Recommender Systems. In: AAAI Workshop on Semantic Web Personalization, San Jose, USA (2004)
Linden, G., Smith, B., York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing (January / February 2003)
Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-Commerce. In: Proceedings of the 2nd ACM conference on Electronic commerce, Minneapolis, Minnesota, United States, October 17-20, pp. 158–167 (2000)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: CSCW 1994. Conference on Computer Supported Cooperative Work (Chapel Hill, 1994), pp. 175–186. ACM, New York (1994)
Good, N., Konstan, J.B., Borchers, J.A., Sarwar, B., Herlocker, J., Riedl, J.: Combining Collaborative Filtering with Personal Agents for Better Recommendations. In: AAAI 1999. Proceedings of the 1999 Conference of the American Association for Artificial Intelligence, pp. 439–446 (1999)
Tung, H.-W., Soo, V.-W.: A Personalized Restaurant Recommender Agent for Mobile E-Service. In: EEE 2004. Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (2004)
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Yamashita, A., Kawamura, H., Iizuka, H., Ohuchi, A. (2007). Effect of the Number of Users and Bias of Users’ Preference on Recommender Systems. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_111
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DOI: https://doi.org/10.1007/978-3-540-77226-2_111
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
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