Eigenvalue Perturbation for Item-based Recommender Systems
Pages 656 - 660
Abstract
Adding confidence estimates to predicted ratings has been shown to positively influence the quality of the recommendations provided by a recommender system. While confidence over single point predictions of ratings and preferences has been widely studied in literature, limited effort has been put in exploring the benefits provided by user-level confidence indices. In this work we exploit a recently introduced user-level confidence index, called eigenvalue confidence index, in order to provide maximum confidence recommendations for item-based recommender systems. We firstly derive a closed form solution to calculate the index, then we propose a new recommendation methodology for item-based models, called eigenvalue perturbation, founded on the strongly positive correlation between the index value and the accuracy of the recommendations. We show and discuss the accuracy results obtained with a comprehensive set of experiments over several datasets and using different item-based models, empirically proving that applying the new technique we are able to outperform the original recommendation models in most of the experimental configurations.
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September 2021
883 pages
ISBN:9781450384582
DOI:10.1145/3460231
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New York, NY, United States
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Published: 13 September 2021
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RecSys '21: Fifteenth ACM Conference on Recommender Systems
September 27 - October 1, 2021
Amsterdam, Netherlands
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Overall Acceptance Rate 254 of 1,295 submissions, 20%
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