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.
Highlights
Recommender systems (RS) represent a critical component of B2C online services
This fact is because the evaluation of the performance function depends on a series of random factors related to the stochastic gradient descent algorithm and on how the dataset is divided in training and validation set
We used the Collaborative filtering approaches (CF) matrix-factorization method, which leads to an optimization problem that can be solved through the stochastic gradient descent algorithm
Summary
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. The model-based methods are an alternative approach that try to predict the ratings by characterizing both items and users using a certain number of parameters inferred from the rating patterns More in details, they are based on the assumptions that the preferences of a user can be inferred from a small number of hidden or latent factors. Alternative use of BO for RS can be found in Vanchinathan et al (2014), where the challenge of ranking recommendation lists based on click feedback by efficiently encoding similarities among users and among items is considered In this case, the Gaussian Process is used to model the elements of the rating matrix directly. We want to use BO for the hyper-parameter optimization related to the parameters of the stochastic gradient descent to find the best possible configuration, in terms of the learning rate, number of latent factors, and regularization parameter. A benchmark application is presented in Sect. 4, and Sect. 5 we present the conclusions
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