Collaborative Filtering Technical Comparison in Implicit Data

Recommendation systems have become a necessity due to the mass of information accumulated for each site. For this purpose, there are several methods including collaborative filtering and content-based filtering. For each approach there is a vast list of procedural choices. The work studies the different methods and algorithms in the field of collaborative filtering recommendation. The objective of the work is to implement these algorithms in order to compare the different performances of each one; the tests were carried out in two datasets, book crossing and Movieslens. The use of a data set benchmark is crucial for the proper evaluation of collaborative filtering algorithms in order to draw a conclusion on the performance of the algorithms.