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
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Lightweight Collaborative Filtering Method for Binary-Encoded Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Some simplified NP-complete problems
STOC '74 Proceedings of the sixth annual ACM symposium on Theory of computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Extraction of customer potential value using unpurchased items and in-store movements
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part III
Vintage radio interface: analog control for digital collections
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Intelligent Decision Technologies - Special issue on Multimedia/Multimodal Human-Computer Interaction in Knowledge-based Environments
A general collaborative filtering framework based on matrix bordered block diagonal forms
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Improve collaborative filtering through bordered block diagonal form matrices
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Collaborative filtering is used for the prediction of user preferences in recommender systems, such as for recommending movies, music, or articles. This method has a good effect on a company's business. E-commerce companies such as Amazon and Netflix have successfully used recommender systems to increase sales and improve customer loyalty. However, these systems generally require ratings for the movies, music, etc. It is usually difficult or expensive to obtain such ratings data comparison with transaction data. Therefore, we need a high quality recommender system that uses only historical purchasing data without ratings. This paper discusses the effectiveness of a graph-partitioning method based recommender system. In numerical computational experiments, we applied our method to the purchasing data for CDs, and compared our results with those obtained by a traditional method. This showed that our method is more practical for business.