Hyperparameter optimization for recommender systems through Bayesian optimization
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Hyperparameter optimization for recommender systems through Bayesian optimization B. G. Galuzzi1
· I. Giordani1 · A. Candelieri1 · R. Perego1 · F. Archetti1,2
Received: 17 February 2019 / Accepted: 2 September 2020 © The Author(s) 2020
Abstract Recommender systems represent one of the most successful applications of machine learning in B2C online services, to help the users in their choices in many web services. 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. Keywords Bayesian optimization · Collaborative filtering · Hyperparameters optimization · Matrix factorization · Recommender system
1 Introduction Recommender systems (RS) represent a critical component of B2C online services. They improve the customer experience exposing contents of which customer are still unaware and attempt to profile user preferences. More in details, an RS aims to recommend items (movies, songs, books, etc.) that fit the user’s preferences, to help the user
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B. G. Galuzzi [email protected]
1
Department of Computer Science, Systems and Communications, University of Milano-Bicocca, viale Sarca 336, 20125 Milan, Italy
2
Consorzio Milano-Ricerche, via Roberto Cozzi, 53, 20126 Milan, Italy
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Fig. 1 Two different types of ML algorithm for RS: a Content-based approach, and b Collaborative filtering approach. Source: https://towardsdatascience.com/brief-on-recommender-systems-b86a1068a4dd
in selecting items from a large set of choices. Example of applications can be found in many fields, among which movies (Koren et al. 2009), music (Lee et al. 2010), books (Crespo et al. 2011), e-commerce (McNally et al. 2011) and active stock selection (De Rossi et al. 2019). The idea behind an RS is that providing personalized suggestions significantly increasing the likelihood of a customer making a purchase compared to un-personalized ones. Personalized recommendations have huge importance where the number of possible items is large such as in e-commerce related to art (books, movies, music), fashion, food, etc. Some of the major participants in e-commerce (Amazon), movie streaming (Netflix),
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