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Algorithms for Social Recommendation

Chapter

Abstract

Recommender systems aim to filter information for the user, usually based on personalization techniques. As social media becomes more popular, the need for recommender systems to help each user reach the most attractive and relevant information becomes acute. Social recommender systems provide just that: filtering social content, activities, tags, people, and communities, and suggesting them for the user. The social web offers many new forms of data and metadata for social recommendation algorithms to take advantage of. In this chapter, we will review different social recommendation algorithms and their way to exploit different types of social data and metadata to enhance their effectiveness.

Keywords

Social Media Recommender System Social Network Site News Item Probabilistic Latent Semantic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

With thanks to David Carmel, Jilin Chen, Tal Daniel, Casey Dugan, Werner Geyer, Michal Jacovi, Michael Muller, Shila Ofek-Koifman, Adam Perer, Ariel Raviv, Inbal Ronen, Sigalit Ur, Erel Uziel, Eric Wilcox, Sivan Yogev, and Naama Zwerdling for jointly working on the studies described in this chapter.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.IBM ResearchHaifaIsrael

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