With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG.
Text Mining Recommendation Systems/ Collaborative Filtering, Structure Web Graph Page Rank/Spam Social Networking, Data Structures Bloom Filters ... Stanford University course; resources, links, more.
S. Li, A. Karatzoglou, und C. Gentile. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Seite 539--548. New York, NY, USA, ACM, (2016)
J. Kunegis, S. Schmidt, \. Albayrak, C. Bauckhage, und M. Mehlitz. Proceedings of the 2008 Conference on ECAI 2008: 18th European Conference on Artificial Intelligence, Seite 261--265. Amsterdam, The Netherlands, IOS Press, (2008)
E. Diaz-Aviles, M. Georgescu, und W. Nejdl. Proceedings of the sixth ACM conference on Recommender systems, Seite 229--232. New York, NY, USA, ACM, (2012)
V. Raykar, R. Duraiswami, und B. Krishnapuram. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, Seite 385--392. (2007)
R. Salakhutdinov, A. Mnih, und G. Hinton. Proceedings of the 24th international conference on Machine learning, Seite 791--798. New York, NY, USA, ACM, (2007)
S. Balakrishnan, und S. Chopra. Proceedings of the fifth ACM international conference on Web search and data mining, Seite 143--152. New York, NY, USA, ACM, (2012)
N. Liu, und Q. Yang. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, Seite 83--90. New York, NY, USA, ACM, (2008)
K. Chen, T. Chen, G. Zheng, O. Jin, E. Yao, und Y. Yu. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, Seite 661--670. New York, NY, USA, ACM, (2012)
X. Lam, T. Vu, T. Le, und A. Duong. Proceedings of the 2nd international conference on Ubiquitous information management and communication, Seite 208--211. New York, NY, USA, ACM, (2008)
A. Schein, A. Popescul, L. Ungar, und D. Pennock. Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, Seite 253--260. New York, NY, USA, ACM, (2002)
Y. Koren. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, Seite 447--456. New York, NY, USA, ACM, (2009)
T. Kamishima. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, Seite 583--588. New York, NY, USA, ACM, (2003)
D. Stern, R. Herbrich, und T. Graepel. Proceedings of the 18th international conference on World wide web, Seite 111--120. New York, NY, USA, ACM, (2009)
A. Menon, und C. Elkan. Proceedings of the 2010 IEEE International Conference on Data Mining, Seite 364--373. Washington, DC, USA, IEEE Computer Society, (2010)