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Title: Improving collaborative filtering's rating prediction coverage in sparse datasets by exploiting the 'friend of a friend' concept

Authors: Dionisis Margaris; Costas Vassilakis

Addresses: Department of Informatics and Telecommunications, University of Athens, Athens, Greece ' Department of Informatics and Telecommunications, University of the Peloponnese, Tripoli, Greece

Abstract: In collaborative filtering users with highly similar tastes are termed 'near neighbours' and recommendations for a user are based on her 'near neighbours' ratings. However, for a number of users no near neighbours can be found, a problem termed as the 'grey sheep' problem. This problem is more intense in sparse datasets. In this work, we propose an algorithm for alleviating this problem by exploiting the friend of a friend (FOAF) concept. The proposed algorithm, CFfoaf, has been evaluated against eight widely used sparse datasets and under two widely used collaborative filtering correlation metrics, namely the Pearson Correlation Coefficient and the Cosine Similarity and has been proven to be particularly effective.

Keywords: collaborative filtering; recommender systems; big data; sparse datasets; similarity transitivity; friend-of-a-friend; Pearson correlation coefficient; cosine similarity; prediction accuracy; coverage; evaluation.

DOI: 10.1504/IJBDI.2020.106178

International Journal of Big Data Intelligence, 2020 Vol.7 No.1, pp.47 - 57

Received: 25 Feb 2019
Accepted: 26 May 2019

Published online: 01 Apr 2020 *

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