Collaborative filtering employing users’ interactions in web applications

Abstract

Currently, thanks to the Internet anyone has access to a large amount of data and for this reason it is essential to create new systems that help to understand that information in a little time. Recommender Systems are engines which allow to filter the information depending on people’s interests. There are different kinds of Recommenders and each of them has a different purpose. In this project, a case of use of a Collaborative filtering Recommender System is introduced employing every interaction users do while surfing the Stilavia web site as data input. In order to carry out this task, some scoring functions are required to generate a model. This model will be extrapolated throughout the whole dataset space thanks to a Machine Learning algorithm called Alternating Least Squares (ALS) that is available in a library of the Apache Spark framework. Lastly, the results of each scoring function will be tested and evaluated employing a statistic estimator.