Algorithms for clustering data
Algorithms for clustering data
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
An MDP-Based Recommender System
The Journal of Machine Learning Research
Improving Accuracy of Recommender System by Clustering Items Based on Stability of User Similarity
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
IIS '09 Proceedings of the 2009 International Conference on Industrial and Information Systems
An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Least squares quantization in PCM
IEEE Transactions on Information Theory
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With the development of electronic commerce, a lot of recommendation techniques has been developed. Collaborative filtering(CF) is one of the most important technologies. However, traditional collaborative filtering suffers sparsity and scalability problems, which results in poor quality of prediction in recommendation systems. To solve these problems, this paper proposed a recommendation algorithm combining clustering method and slope one scheme. This approach uses clustering algorithms to partition the set of items to several clusters based on user rating data, and then we use slope one scheme to predict ratings independently for unknown items based on which cluster the items belong to. We make experiments on the standard benchmark Movielens data sets and compare our approach with the basic slope one scheme. The results show that our algorithm outperforms the slope one scheme.