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

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.