Like like alike---Joint Friendship and Interest Propagation in Social Networks

by Shuang Hong Yang, Bo Long, Alex Smola, Narayanan Sadagopan, Zhaohui Zheng and Hongyuan Zha

This paper extends the idea of RLFM: we interpret RLFM with a supervised learning problem and this paper extends this idea with a social net, mainly via regularizers. So that's why the probabilistic model in RLFM may not be that important (just an interpretation in a disciplined way). There are 7 regularizers in total, which leads to a very complicated objective function.

The optimization is solved via stochastic gradient descent. Maybe for other techniques like interior point method, it is too difficult to derive?