Graph-based Rating Prediction Using Eigenvector Centrality

The most of recommendation systems rely on the statistical correlations of the past explicitly given user rating for items (e.g. collaborative filtering). However, in conditions of insufficient data of past rating activities, these systems are facing difficulties in rating prediction, this situation is commonly known as the cold-start problem. This paper describes how graph-based represendation and Social Network Analysis can be used to help dealing with cold-start problem. We proposed a method to predict user rating based on the hypotesis that the rating of the node in the network corresponded to the rating of the most important nodes which are connected to it. The proposed method has been particularly applied to three MovieLens datasets to evaluate rating predition performance. Obtained results showed competitiveness of our method.