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
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
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
Building a Recommender Agent for e-Learning Systems
ICCE '02 Proceedings of the International Conference on Computers in Education
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A Multi-Agent Open Architecture for a TV Recommender System: A Case Study Using a Bayesian Strategy
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Implicit: an agent-based recommendation system for web search
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Incremental collaborative filtering for highly-scalable recommendation algorithms
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
A user-item relevance model for log-based collaborative filtering
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
Improving the scalability of recommender systems by clustering using genetic algorithms
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
An Adaptive Match-Making System reflecting the explicit and implicit preferences of users
Expert Systems with Applications: An International Journal
Cluster searching strategies for collaborative recommendation systems
Information Processing and Management: an International Journal
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Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches trying to apply agent technology to RS. Collaborative Filtering, one of the most widely used approach to predict user's ratings in Recommender System, predicts a user's rating towards an item by aggregating ratings given by users who have similar preference to that user. In existing approaches, user similarity is often computed on the whole set of items. However, because the number of items is often very large and so is the diversity among items, users who have similar preference in one category may have totally different judgement on items of another kind. In order to deal with this problem, we propose a method to cluster items, so that inside a cluster, similarity between users does not change significantly from item to item. After the item clustering phase, when predicting rating of a user towards an item, we only aggregate ratings of users who have similarity preference to that user inside the cluster of that item. Experiments evaluating our approach are carried out on the real dataset taken from MovieLens, a movies recommendation web site. Experiment results suggest that our approach can improve prediction accuracy compared to existing approaches.