Recommending items to group of users using Matrix Factorization based Collaborative Filtering

Abstract

Group recommender systems are becoming very popular in the social web owing to their ability to provide a set of recommendations to a group of users. Several group recommender systems have been proposed by extending traditional KNN based Collaborative Filtering. In this paper we explain how to perform group recommendations using Matrix Factorization (MF) based Collaborative Filtering (CF). We propose three original approaches to map the group of users to the latent factor space and compare the proposed methods in three different scenarios: when the group size is small, medium and large. We also compare the precision of the proposed methods with state-of-the-art group recommendation systems using KNN based Collaborative Filtering. We analyze group movie ratings on MovieLens and Netflix datasets. Our study demonstrates that the performance of group recommender systems varies depending on the size of the group, and MF based CF is the best option for group recommender systems.