  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.
