Eigenvalue Perturbation for Item-based Recommender Systems
- 13 September 2021
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM) in Fifteenth ACM Conference on Recommender Systems
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.Keywords
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