Learning Image and User Features for Recommendation in Social Networks

no code implementations • ICCV 2015 • Xue Geng, Hanwang Zhang, Jingwen Bian, Tat-Seng Chua

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.

Recommendation Systems