Title: Weighted Random Walk Sampling for Multi-Relational Recommendation,
Abstract: In the information overloaded web, personalized recommender systems are
essential tools to help users find most relevant information. The most
heavily-used recommendation frameworks assume user interactions that are
characterized by a single relation. However, for many tasks, such as
recommendation in social networks, user-item interactions must be modeled as a
complex network of multiple relations, not only a single relation. Recently
research on multi-relational factorization and hybrid recommender models has
shown that using extended meta-paths to capture additional information about
both users and items in the network can enhance the accuracy of recommendations
in such networks. Most of this work is focused on unweighted heterogeneous
networks, and to apply these techniques, weighted relations must be simplified
into binary ones. However, information associated with weighted edges, such as
user ratings, which may be crucial for recommendation, are lost in such
binarization. In this paper, we explore a random walk sampling method in which
the frequency of edge sampling is a function of edge weight, and apply this
generate extended meta-paths in weighted heterogeneous networks. With this
sampling technique, we demonstrate improved performance on multiple data sets
both in terms of recommendation accuracy and model generation efficiency.