Abstract
Recommender systems aim to estimate the judgment or opinion that a user might offer to an item. Matrix-factorization-based collaborative filtering typifies both users and items as vectors of factors inferred from item rating patterns. This method finds latent structure in the data, assuming that observations lie close to a low-dimensional latent space. However, matrix factorizations have been traditionally designed by hand. Here, we present Evolutionary Matrix Factorization (EMF), an evolutionary approach that automatically generates matrix factorizations aimed at improving the performance of recommender systems. Initial experiments using this approach show that EMF generally outperforms baseline methods when applied to MovieLens and FilmTrust datasets, having a similar performance to those baselines on the worst cases. These results serve as an incentive to continue improving and studying the application of an evolutionary approach to collaborative filtering based on Matrix Factorization.
Highlights
In the digital world in which we currently live, we are constantly flooded with large amounts of information that we are unable to assimilate
The main goal of the proposed method is to improve the accuracy of the predictions performed with Matrix Factorization (MF)-based Collaborative Filtering (CF) by evolving the aggregation function h that combines the user and item latent factors using Genetic Programming (GP)
This paper presents a new approach to improve the quality of predictions provided by MF models applied to CF-based Recommender Systems (RSs)
Summary
In the digital world in which we currently live, we are constantly flooded with large amounts of information that we are unable to assimilate Faced with such a large amount of information, those systems capable of filtering and customizing it for each user have become increasingly important in recent years. A RS is a subcategory of the information filtering process that aims to estimate the judgment or opinion that a customer might offer to an item. Whether it is generating playlists for Spotify, recommending products to buy on Amazon, or suggesting the series you will see on Netflix, RSs are a part of our daily lives. A RS should be defined as an intelligent system capable of providing each user with a personalized list of products or services that may be of interest [5,6]
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