Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
The Wealth of Networks: How Social Production Transforms Markets and Freedom
The Wealth of Networks: How Social Production Transforms Markets and Freedom
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Scalable proximity estimation and link prediction in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Link Prediction Using BenefitRanks in Weighted Networks
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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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.