GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Links: Java resource for artificial intelligence
intelligence
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
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
An Application of Social Filtering to Movie Recommendation
Software Agents and Soft Computing: Towards Enhancing Machine Intelligence, Concepts and Applications
Improving Case-Based Recommendation: A Collaborative Filtering Approach
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Promoting Recommendations: An Attack on Collaborative Filtering
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
PTV: Intelligent Personalised TV Guides
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
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Information is becoming increasingly available in digital formats such as Web Pages, MP3 files and many others. This puts more emphasis on the need for reliable information filtering techniques. New recommendation algorithms are continuously being developed to deal with the problem of information overload. In this paper we present a new, regression-based approach to the application of recommendation algorithms. We classify five different datasets based on a range of metrics, including sparsity, user-item ratio and the distribution of user ratings. From performance analysis tests of four predictive algorithms over these sets, we develop a regression function to predict the suitability of a particular recommendation algorithm for a previously unseen dataset. Our results show that the best-performing algorithm on the new set is the one predicted by our regression analysis.