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.