A multi-theoretical kernel-based approach to social network-based recommendation

TL;DR

This paper introduces a multi-theoretical kernel-based machine learning approach for social network-based recommendations, integrating various social theories to improve accuracy over traditional trust-based and collaborative filtering methods.

Contribution

It systematically models multiple social network theories using kernels and combines them with a non-linear multiple kernel learning algorithm for enhanced recommendations.

Findings

Our approach outperforms trust-based methods and collaborative filtering in accuracy.

Kernels from contagion and homophily theories significantly improve the model.

The method effectively captures complex social influences on user preferences.

Abstract

Recommender systems are a critical component of e-commercewebsites. The rapid development of online social networking services provides an opportunity to explore social networks together with information used in traditional recommender systems, such as customer demographics, product characteristics, and transactions. It also provides more applications for recommender systems. To tackle this social network-based recommendation problem, previous studies generally built trust models in light of the social influence theory. This study inspects a spectrumof social network theories to systematicallymodel themultiple facets of a social network and infer user preferences. In order to effectively make use of these heterogonous theories, we take a kernel-based machine learning paradigm, design and select kernels describing individual similarities according to social network theories, and employ a non-linear multiple kernel learning algorithm to combine the kernels into a unified model. This design also enables us to consider multiple theories' interactions in assessing individual behaviors. We evaluate our proposed approach on a real-world movie review data set. The experiments show that our approach provides more accurate recommendations than trust-based methods and the collaborative filtering approach. Further analysis shows that kernels derived from contagion theory and homophily theory contribute a larger portion of the model.