Ryadh Dahimene
ABSTRACT
Social networks have become an important information source. Due to their unprecedented success, these systems have to face an exponentially increasing amount of user generated content. As a consequence, finding relevant users or data matching specific interests is a challenging. We present RecLand, a recommender system that takes advantage of the social graph topology and of the existing contextual information to recommend users. The graphical interface of RecLand shows recommendations that match the topical interests of users and allows to tune the parameters to adapt the recommendations to their needs.
- J. A. Aslam and M. Montague. Models for metasearch. In SIGIR, pages 276--284, 2001. Google Scholar
Digital Library
- A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: Scalable online collaborative filtering. In WWW, pages 271--280, 2007. Google ScholarDigital Library
- P. Gupta, A. Goel, J. Lin, A. Sharma, D. Wang, and R. Zadeh. WTF: the Who to Follow Service at Twitter. In WWW, pages 505--514, 2013. Google ScholarDigital Library
- I. Konstas, V. Stathopoulos, and J. M. Jose. On social networks and collaborative recommendation. In SIGIR, pages 195--202, 2009. Google ScholarDigital Library
- D. Liben-Nowell and J. Kleinberg. The Link Prediction Problem for Social Networks. In CIKM, pages 556--559, 2003. Google ScholarDigital Library
- K. Tretyakov, A. Armas-Cervantes, L. García-Bañuelos, J. Vilo, and M. Dumas. Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs. In CIKM, pages 1785--1794, 2011. Google ScholarDigital Library
- J. Weng, E.-P. Lim, J. Jiang, and Q. He. TwitterRank: Finding Topic-sensitive Inuential Twitterers. In WSDM, pages 261--270, 2010. Google ScholarDigital Library
Index Terms
- RecLand: A Recommender System for Social Networks
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