Learning Disentangled Representations for Recommendation

NeurIPS 2019 Jianxin MaChang ZhouPeng CuiHongxia YangWenwu Zhu

User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users' decision making processes. The factors are highly entangled, and may range from high-level ones that govern user intentions, to low-level ones that characterize a user's preference when executing an intention... (read more)

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