Exploring Social Influence for Recommendation - A Probabilistic Generative Model Approach

TL;DR

This paper introduces a probabilistic generative model that integrates social influence with collaborative filtering and content-based methods for improved item recommendation, validated through large-scale experiments.

Contribution

It presents a novel unified probabilistic model for social influence in recommendation systems and develops scalable algorithms for large datasets.

Findings

Social influence significantly improves recommendation accuracy.

The model outperforms existing methods in experiments.

Users on whrrl.com are more influenced by friends than on last.fm.

Abstract

In this paper, we propose a probabilistic generative model, called unified model, which naturally unifies the ideas of social influence, collaborative filtering and content-based methods for item recommendation. To address the issue of hidden social influence, we devise new algorithms to learn the model parameters of our proposal based on expectation maximization (EM). In addition to a single-machine version of our EM algorithm, we further devise a parallelized implementation on the Map-Reduce framework to process two large-scale datasets we collect. Moreover, we show that the social influence obtained from our generative models can be used for group recommendation. Finally, we conduct comprehensive experiments using the datasets crawled from last.fm and whrrl.com to validate our ideas. Experimental results show that the generative models with social influence significantly outperform those without incorporating social influence. The unified generative model proposed in this paper obtains the best performance. Moreover, our study on social influence finds that users in whrrl.com are more likely to get influenced by friends than those in last.fm. The experimental results also confirm that our social influence based group recommendation algorithm outperforms the state-of-the-art algorithms for group recommendation.