Context-aware graph-based recommendations exploiting Personalized PageRank

Abstract In this article we present a context-aware recommendation method that exploits graph-based data models and Personalized PageRank to provide users with recommendations. In particular, our approach extends the basic graph-based representation that relies on users and items nodes by introducing a third class of nodes, that is to say, context nodes, whose goal is to model the different contextual situations in which an item can be consumed. Given such a data model, we used Personalized PageRank to identify the most suitable recommendations for each user: in a nutshell, our model is based on the intuition that context nodes shall be used to influence random walks, in order to assist the algorithm in identifying the items that are relevant in a particular contextual setting. In the experimental evaluation we investigated the effectiveness of the approach on three different datasets. The results showed that our context-aware graph-based approach overcame the baselines in most of the experimental settings and obtained the best overall results in cold-start situations, thus confirming the validity of the methodology.