Weighted Random Walk Sampling for Multi-Relational Recommendation

TL;DR

This paper introduces a weighted random walk sampling method for multi-relational recommendation systems that preserves edge weight information, leading to improved accuracy and efficiency in recommendations.

Contribution

It proposes a novel weighted random walk sampling technique that better captures weighted relations in heterogeneous networks for recommendation tasks.

Findings

Enhanced recommendation accuracy on multiple datasets

Improved model generation efficiency

Effective preservation of edge weight information

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.