What have the neighbours ever done for us? A collaborative filtering perspective

Collaborative filtering (CF) techniques have proved to be a powerful and popular component of modern recommender systems.
Common approaches such as user-based and item-based methods generate predictions from the past ratings of users by combining two separate ratings components: a base estimate, generally based on the average rating of the target user or item, and a neighbourhood estimate, generally
based on the ratings of similar users or items. The common assumption
is that the neighbourhood estimate gives CF techniques a considerable
edge over simpler average-rating techniques. In this paper we examine
this assumption more carefully and demonstrate that the influence of
neighbours can be surprisingly minor in CF algorithms, and we show
how this has been disguised by traditional approaches to evaluation,
which, we argue, have limited progress in the field.