Using collaborative filtering to weave an information tapestry
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
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Fuzzy logic methods in recommender systems
Fuzzy Sets and Systems - Theme: Multicriteria decision
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Evaluating Recommender Systems
AXMEDIS '08 Proceedings of the 2008 International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution
Research on Trust-Aware Recommender Model Based on Profile Similarity
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
An Improved Trust Metric for Trust-Aware Recommender Systems
ETCS '09 Proceedings of the 2009 First International Workshop on Education Technology and Computer Science - Volume 01
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Proceedings of the Workshop on Context-Aware Movie Recommendation
Trust based recommender system using ant colony for trust computation
Expert Systems with Applications: An International Journal
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A hybrid recommendation approach for a tourism system
Expert Systems with Applications: An International Journal
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Several approaches for recommending products to the users are proposed in literature, and collaborative filtering has been proved to be one of the most successful techniques. Some issues related to the quality of recommendation and to computational aspects still arise (e.g., cold-start recommendations). In this paper, we investigate the application of model-based Collaborative Filtering (CF) techniques and in particular propose a clustering CF framework and two clustering CF algorithms: Item-based Fuzzy Clustering Collaborative Filtering (IFCCF) and Trust-aware Clustering Collaborative Filtering (TRACCF). We compare several approaches by means of Epinions, MovieLens, Jester, and Poste Italiane datasets (with real customers). Experimental results show an increased value of coverage of the recommendations provided by TRACCF without affecting recommendation quality. Moreover, trust information guarantees high level recommendation for different users.