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Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
[article]
2020
arXiv
pre-print
In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics and has a wide range of applications, such as user profiling, item annotation, and feature-enhanced recommendation. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. Therefore, item recommendation and attribute inference have
arXiv:2005.12021v1
fatcat:dkuv35w5mvd2rofjjmk747vgje