An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering
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Personalisation features are key to the success of many web applications and collaborative recommender systems have been widely implemented. These systems assist users in finding relevant information or products from the vast quantities that are frequently available. In previous work, we have demonstrated that such systems are vulnerable to attack and that recommendations can be manipulated. We introduced the concept of robustness as a performance measure, which is defined as the ability of a system to provide consistent predictions in the presence of noise in the data. In this paper, we expand on our previous work by examining the effects of several neighbourhood formation schemes and similarity measures on system performance. We propose a neighbourhood filtering mechanism for filtering false profiles from the neighbourhood in order to improve the robustness of the system.
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