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2018 | OriginalPaper | Chapter

A Generic Framework for Collaborative Filtering Based on Social Collective Recommendation

Authors : Leschek Homann, Bernadetta Maleszka, Denis Mayr Lima Martins, Gottfried Vossen

Published in: Computational Collective Intelligence

Publisher: Springer International Publishing

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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.

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Title: A Generic Framework for Collaborative Filtering Based on Social Collective Recommendation
Authors: Leschek Homann
Bernadetta Maleszka
Denis Mayr Lima Martins
Gottfried Vossen
Publisher: Springer International Publishing
Book: Computational Collective Intelligence
Print ISBN: 978-3-319-98442-1

Electronic ISBN: 978-3-319-98443-8

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-98443-8_22

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