Data Augmented Maximum Margin Matrix Factorization for Flickr Group Recommendation

User groups on photo sharing websites, such as Flickr, are self-organized communities to share photos and conversations with similar interest and have gained massive popularity. However, the huge volume of groups brings troubles for users to decide which group to choose. Further, directly applying collaborative filtering techniques to group recommendation will suffer from cold start problem since many users do not affiliate to any group. In this paper, we propose a hybrid recommendation approach named Data Augmented Maximum Margin Matrix Factorization (DAMF), by integrating collaborative user-group information and user similarity graph. Specifically, Maximum Margin Matrix Factorization (MMMF) is employed for the collaborative recommendation, while the user similarity graph obtained from the user uploaded images and annotated tags is used as an complementary part to handle the cold start problem and to improve the performance of MMMF. The experiments conducted on our crawled dataset with 2196 users, 985 groups and 334467 images from Flickr demonstrate the effectiveness of the proposed approach.