Replicable Evaluation of Recommender Systems

3 years 2 months ago
Replicable Evaluation of Recommender Systems
Recommender systems research is by and large based on comparisons of recommendation algorithms’ predictive accuracies: the better the evaluation metrics (higher accuracy scores or lower predictive errors), the better the recommendation algorithm. Comparing the evaluation results of two recommendation approaches is however a difficult process as there are very many factors to be considered in the implementation of an algorithm, its evaluation, and how datasets are processed and prepared. This tutorial shows how to present evaluation results in a clear and concise manner, while ensuring that the results are comparable, replicable and unbiased. These insights are not limited to recommender systems research alone, but are also valid for experiments with other types of personalized interactions and contextual information access. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: information filtering, relevance feedback, retrieval models, search process, selec...
Alan Said, Alejandro Bellogín
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Authors Alan Said, Alejandro Bellogín
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