Flickr group recommendation using content interest and social information

Social networks have been an important part of human´s life. Online photo sharing websites like Flickr allow users to experience others´ lifestyles by browsing photos. To gather users who have the same interests, the websites allow users to build their own interest groups and invite other users to join in. A commonly adopted recommendation in social networks such as Sina Microblog uses the social information of users. However, it performs poorly for inactive users. In this paper, we propose a group recommendation scheme by using both the content interest and social information of users. We use tag information, which is not only from users´ photos but also from their favorite photos, to study the content interests of users and use the user-based collaborative filtering for recommendation. The trust-aware collaborative filtering is adopted to study the social information of users for recommendation. Finally, we combine the user-based collaborative filtering and trust-aware collaborative filtering to obtain a promising result on a real-world Flickr dataset.