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Recommender system aims to predict the user preferences from a huge amount of data, basically the past behaviour of the user, using an efficient prediction algorithm. One of the most used is the matrix-factorization algorithm. Like many machine learning algorithms, its effectiveness goes through the tuning of its hyper-parameters, and the associated optimization problem also called hyper-parameter optimization. This represents a noisy time-consuming black-box optimization problem. The related objective function maps any possible hyper-parameter configuration to a numeric score quantifying the algorithm performance. In this work, we show how Bayesian optimization can help the tuning of three hyper-parameters: the number of latent factors, the regularization parameter, and the learning rate. Numerical results are obtained on a benchmark problem and show that Bayesian optimization obtains a better result than the default setting of the hyper-parameters and the random search.<\/jats:p>","DOI":"10.1007\/s10287-020-00376-3","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T21:23:04Z","timestamp":1600809784000},"page":"495-515","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Hyperparameter optimization for recommender systems through Bayesian optimization"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8518-5352","authenticated-orcid":false,"given":"B. 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