Efficient and Secure Collaborative Filtering through Intelligent Neighbour Selection

In this paper, we introduce novel neighbourhood formation and similarity weight transformation schemes for automated collaborative filtering systems. We define profile utility, which models the usefulness of user profiles for collaborative filtering as a function of the items they contain. We demonstrate that our approach leads to more efficient and scalable collaborative filtering when compared to a benchmark k--Nearest Neighbour approach, while providing system accuracy and coverage to the same standard. In particular, we show that our approach is completely secure against the malicious attacks outlined in the paper, whereas k-NN proves very vulnerable