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hello everyone im yu jung from qinghua university its my great honor to present our work disentangling user interest and conformity for recommendation with castle embedding first i will introduce the background of this paper in rock manor systems what we observe are user item interactions however there are causes behind each interaction for example here is a bicycle in e-commerce recommendation and it is a best seller a sports lower may buy the bicycle because of his unique tastes on certain characteristics while an office staff also buys this bicycle simply because of its high sales the two users buy the same bicycle with different causes and in this paper we focus on the two main causes interest and conformity the goal of this paper is to learn disentangled representations for interest and conformity the motivation for learning disentangled representations is to accomplish causal recommendations under non-iid situations this is an example from computer vision and suppose the task i