Fused Matrix Factorization & some personal thoughts

标签：rri   something   ted   int   metrics   nbsp   led   multi   any   

I read this paper, the purpose are common to some extent...but the way this paper has adapted and the way we discussed yesterday still have many differences.

First, when we talked about about friends , we are referring to friends that we deriviated from users‘ checkin data, while the paper refers to social friends. This is a huge difference. And I think there is something to do here. But I recalled one paper I read which has already looked in to location friends (Point-of-Interest Recommendations: Learning Potemtial Check-ins From Friends). This paper looked deeply into three kinds of friends, social friends, location friends(the friends we refer to) and neighbor friends and their influence on user‘s checkin decisions. 

As for another topic we are concerned, there are stil some differences here. The writer used multi center model to calculate the possibilities while we plan to preprocessing the checkin data. It is more like a joint model. But I am not sure whether there are already some research into this.

And both of the papers I mentioned above does not seem to include the possible biases in similarity metrics. 

Fused Matrix Factorization & some personal thoughts

标签：rri   something   ted   int   metrics   nbsp   led   multi   any   

原文地址：http://www.cnblogs.com/fassy/p/7270077.html