Weighted Random Walks for Meta-Path Expansion in Heterogeneous Networks

In social networks, users and items are joined in a complex web of relations, which can be modeled as heterogeneous information networks. Such networks include a variety of object types and the rich relations among them. Recent research has shown that a hybrid recommendation approach combining components built from extended meta-paths in the network can improve the accuracy of recommendations in such networks. However most of this recent work is focused on unweighted heterogeneous networks, and simplifying relations by ignoring weight (including user ratings) loses important information. We propose a random walk based model to generate meta-paths in weighted heterogeneous network in which the frequency of edge sampling is a function of edge weight, and demonstrate that performance is improved using this method.