A Link Prediction Approach to Recommendations in Large-Scale User-Generated Content Systems

Recommending interesting and relevant content from the vast repositories of User-Generated Content systems (UGCs) such as YouTube, Flickr and Digg is a significant challenge. Part of this challenge stems from the fact that classical collaborative filtering techniques - such as k-Nearest Neighbor - cannot be assumed to perform as well in UGCs as in other applications. Such technique has severe limitations regarding data sparsity and scalability that are unfitting for UGCs. In this paper, we employ adaptations of popular Link Prediction algorithms that were shown to be effective in massive online social networks for recommending items in UGCs. We evaluate these algorithms on a large dataset we collect from Flickr. Our results suggest that Link Prediction algorithms are a more scalable and accurate alternative to classical collaborative filtering in the context of UGCs. Moreover, our experiments show that the algorithms considering the immediate neighborhood of users in an user-item graph to recommend items outperform the algorithms that use the entire graph structure for the same. Finally, we find that, contrary to intuition, exploiting explicit social links among users in the recommendation algorithms improves only marginally their performance.