Collaborative Filtering vs. Content-Based Filtering: differences and similarities

TL;DR

This paper compares collaborative filtering and content-based filtering recommendation systems through experiments, highlighting their differences, similarities, and complementary nature across various datasets.

Contribution

It introduces an experimental methodology for comparing recommendation algorithms beyond prediction accuracy, including new content-based algorithms and analysis of their behaviors.

Findings

Collaborative and content-based filtering have complementary strengths.

Experimental results show different behaviors on various datasets.

Content-based algorithms can complement collaborative filtering in recommendation tasks.

Abstract

Recommendation Systems (SR) suggest items exploring user preferences, helping them with the information overload problem. Two approaches to SR have received more prominence, Collaborative Filtering, and Content-Based Filtering. Moreover, even though studies are indicating their advantages and disadvantages, few results empirically prove their characteristics, similarities, and differences. In this work, an experimental methodology is proposed to perform comparisons between recommendation algorithms for different approaches going beyond the "precision of the predictions". For the experiments, three algorithms of recommendation were tested: a baseline for Collaborative Filtration and two algorithms for Content-based Filtering that were developed for this evaluation. The experiments demonstrate the behavior of these systems in different data sets, its main characteristics and especially the complementary aspect of the two main approaches.