A Generic Framework for Collaborative Filtering Based on Social Collective Recommendation

Homann Leschek, Maleszka Bernadetta, Martins Denis, Vossen Gottfried

Abstract

Collaborative filtering has been considered the most used approach for recommender systems in both practice and research. Unfortunately, traditional collaborative filtering suffers from the so-called cold-start problem, which is the challenge to recommend items for an unknown user. In this paper, we introduce a generic framework for social collective recommendations targeting to support and complement traditional recommender systems to achieve better results. Our framework is composed of three modules, namely, a User Clustering module, a Representative module, and an Adaption module. The User Clustering module aims to find groups of users, the Representative module is responsible for determining a representative of each group, and the Adaption module handles new users and assigns them appropriately. By the composition of the framework, the cold-start problem is alleviated.