Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Redefining Clustering for High-Dimensional Applications
IEEE Transactions on Knowledge and Data Engineering
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Explanation in Recommender Systems
Artificial Intelligence Review
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
ACM Transactions on Knowledge Discovery from Data (TKDD)
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
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In this paper, we present a hybrid recommendation approach for discovering potential preferences of individual users. The proposed approach provides a flexible solution that incorporates multidimensional clustering into a collaborative filtering recommendation model to provide a quality recommendation. This facilitates to obtain user clusters which have diverse preference from multi-view for improving effectiveness and diversity of recommendation. The presented algorithm works in three phases: data preprocessing and multidimensional clustering, choosing the appropriate clusters and recommending for the target user. The performance of proposed approach is evaluated using a public movie dataset and compared with two representative recommendation algorithms. The empirical results demonstrate that our proposed approach is likely to trade-off on increasing the diversity of recommendations while maintaining the accuracy of recommendations.