kNN CF

Recommender systems, based on collaborative filtering, draw their strength from techniques that manipulate a set of user-rating profiles in order to compute predicted ratings of unrated items. There are a wide range of techniques that can be applied to this problem; however, the k-nearest neighbour (kNN) algorithm has become the dominant method used in this context. Much research to date has focused on improving the performance of this algorithm, without considering the properties that emerge from manipulating the user data in this way. In order to understand the effect of kNN on a user-rating dataset, the algorithm can be viewed as a process that generates a graph, where nodes are users and edges connect similar users: the algorithm generates an implicit social network amongst the system subscribers. Temporal updates of the recommender system will impose changes on the graph. In this work we analyse user-user kNN graphs from a temporal perspective, retrieving characteristics such as dataset growth, the evolution of similarity between pairs of users, the volatility of user neighbourhoods over time, and emergent properties of the entire graph as the algorithm parameters change. These insights explain why certain kNN parameters and similarity measures outperform others, and show that there is a surprising degree of structural similarity between these graphs and explicit user social networks.