Personalized and geo-referenced image recommendation using unified hypergraph learning and group sparsity optimization

The rapid development of social media has led to a surge of interest in multimedia recommendation. Several recommender systems have been developed, but achieving a satisfactory efficiency or accuracy still remains an open problem. In this paper, a novel multi-reference image recommendation system is proposed based on a unified hypergraph. Relevant images from a large pool are recommended to a reference user or a reference geo-location. In addition to that, the hypergraph ranking problem is enhanced by enforcing group sparsity constraints. By adjusting the different weights associated to the object groups, we control each object group effect in the recommendation process. Experiments on a dataset crawled from Flickr demonstrate the merits of the proposed method.