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A Study of Evaluation Metrics for Recommender Algorithms

Redpath, Jennifer, Shapcott, CM, McClean, SI and Chen, Liming (2008) A Study of Evaluation Metrics for Recommender Algorithms. In: The 19th Irish Conference on Artificial Intelligence and Cognitive Science. Irish AI society. 10 pp. [Conference contribution]

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There are inherent problems with evaluating the accuracy of recommender systems. Commonly-used metrics for recommender systems depend on the number of recommendations produced and the number of hidden items withheld, making it difficult to directly compare one system with another. In this paper we compare recommender algorithms using two datasets; the standard MovieLens set and an e-commerce dataset that has implicit ratings based on browsing behaviour. We introduce a measure that aids in the comparison and show how to compare results with baseline predictions based on random recommendation selections.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:17866
Deposited By: Dr Liming Chen
Deposited On:12 Apr 2011 14:47
Last Modified:09 May 2016 11:06

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