Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Modern Information Retrieval
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multidimensional Recommender Systems: A Data Warehousing Approach
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
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
Scale and Translation Invariant Collaborative Filtering Systems
Information Retrieval
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Memory-based collaborative filtering (CF) is widely used in the recommendation system based on the similar users or items. But all of these approaches suffer from data sparsity. In many cases, the user-item matrix is quite sparse, which directly leads to inaccurate recommend results. This paper focuses the memory-based collaborative filtering problem on the factor: missing data processing. We propose an advance missing data processing includes two steps: (1) using enhanced CHARM algorithm for mining closed subsets --- group of users that share interest in some items, (2) using adjusted Slope One algorithm base on subsets for utilizing not only information of both users and items but also information that fall neither in the user array nor in the item array. After that, we use Pearson Correlation Coefficient algorithm for predicting rating for active user. Finally, the empirical evaluation results reveal that the proposed approach outperforms other state-of-the-art CF algorithms and it is more robust against data sparsity.