Learning Image and User Features for Recommendation in Social Networks

ICCV 2015 Xue GengHanwang ZhangJingwen BianTat-Seng Chua

Good representations of data do help in many machine learning tasks such as recommendation. It is often a great challenge for traditional recommender systems to learn representative features of both users and images in large social networks, in particular, social curation networks, which are characterized as the extremely sparse links between users and images, and the extremely diverse visual contents of images... (read more)

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