The Effect of the Personalized Settings for CF-Based Recommender Systems

다국어 초록 (Multilingual Abstract)

In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations with ‘training’ and ‘verification’ datasets. The method is compared with traditional ‘constant settings’ methods using Netflix data. The results show that the new method outperforms traditional, ordinary CF. Implications and future research directions are also discussed.

참고문헌 (Reference)

1 Burke, R., "Semantic ratings and heuristic similarity for collaborative filtering, in AAAI Technical Report" 14-20, 2000

2 Schafer, J. B., "Metarecommendation systems : User-controlled integration of diverse recommendations, in CIKMʼ02" ACM 2002

3 Konstan, J. A, "GroupLens : Applying collaborative filtering to Usenet news" 40 (40): 77-87, 1997

4 Breese, J. S, "Empirical analysis of predictive algorithms for collaborative filtering" 1998

5 Xiao, B., "E-commerce product recommendation agents : Use, characteristics, and impact" 31 (31): 137-209, 2007

6 Im, I, "Does a one-size recommendation system fit all? : The effectiveness of collaborative filtering based recommendation systems across different domains and search modes" 26 (26): 2007

7 Riedl, J, "Combining collaborative filtering with personal agents for better recommendations" 1999

8 Bell, R, "Chasing $1,000,000 : How we won the Neflix progressive prize" 18 (18): 4-12, 2007

9 Cho, Y, "Applying centrality analysis to solve the cold-start and sparsity problems in collaborative filtering" 17 (17): 99-114, 2011

10 Herlocker, J. L., "An algorithmic framework for performing collaborative filtering, in Conference on Research and Development in Information Retrieval" ACM Press 1999

11 Shahabi, C., "An adaptive recommendation system without explicit acquisition of user relevance feedback" 14 : 173-192, 2003

12 Lee, T. Q, "A similarity measure for collaborative filtering with implicit feedback" Springer-Verlag 385-397, 2007

13 Christakou, C, "A hybrid movie recommender system based on neural networks, in International Conference on Intelligent Systems Design and Applications" Wroclaw 2005

14 박득희, "A Literature Review and Classification of Recommender Systems on Academic Journals" 한국지능정보시스템학회 17 (17): 139-152, 2011