Inferring users' preferences through leveraging their social relationships

TL;DR

This paper introduces Social Mass Diffusion, a method that leverages social relationships to improve recommendation accuracy, especially for new users, by integrating social networks with user-item interactions.

Contribution

The paper proposes a novel social-aware recommendation algorithm, SMD, that enhances accuracy and addresses cold-start issues by combining social and bipartite networks.

Findings

SMD outperforms traditional mass diffusion in accuracy.

Significant improvement for small degree users.

Effective in cold-start scenarios for new users.

Abstract

Recommender systems, inferring users' preferences from their historical activities and personal profiles, have been an enormous success in the last several years. Most of the existing works are based on the similarities of users, objects or both that derived from their purchases records in the online shopping platforms. Such approaches, however, are facing bottlenecks when the known information is limited. The extreme case is how to recommend products to new users, namely the so-called cold-start problem. The rise of the online social networks gives us a chance to break the glass ceiling. Birds of a feather flock together. Close friends may have similar hidden pattern of selecting products and the advices from friends are more trustworthy. In this paper, we integrate the individual's social relationships into recommender systems and propose a new method, called Social Mass Diffusion (SMD), based on a mass diffusion process in the combined network of users' social network and user-item bipartite network. The results show that the SMD algorithm can achieve higher recommendation accuracy than the Mass Diffusion (MD) purely on the bipartite network. Especially, the improvement is striking for small degree users. Moreover, SMD provides a good solution to the cold-start problem. The recommendation accuracy for new users significantly higher than that of the conventional popularity-based algorithm. These results may shed some light on the new designs of better personalized recommender systems and information services.