Abstract
Social tagging systems have become increasingly a popular way to organize online heterogeneous resources. Tag recommendation is a key feature of social tagging systems. Many works has been done to solve this hard tag recommendation problem and has got same good results these years. Taking into account the complexity of the tagging actions, there still exist many limitations. In this paper, we propose a probabilistic model to solve this tag recommendation problem. The model is based on Bayesian principle, and it’s very robust and efficient. For evaluating our proposed method, we have conducted experiments on a real dataset extracted from BibSonomy, an online social bookmark and publication sharing system. Our performance study shows that our method achieves good performance when compared with classical approaches.
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References
Biancalana, C., Micarelli, A., Squarcella, C.: Nereau: a social approach to query expansion. In: Proceedings of the 10th ACM Workshop on Web Information and Data Management (2008)
Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., Su, Z.: Optimizing web search using social annotations. In: Proceedings of the 16th International Conference on World Wide Web (2007)
Schmidt, K.-U., Sarnow, T., Stojanovic, L.: Socially filtered web search: an approach using social bookmarking tags to personalize web search. In: Proceedings of the 24th ACM Symposium on Applied Computing (2009)
Xu, S., Bao, S., Fei, B., Su, Z., Yu, Y.: Exploring folksonomy for personalized search. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2008)
Yin, Z., Li, R., Mei, Q., Han, J.: Exploring social tagging graph for web object classification. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009)
Ramage, D., Heymann, P., Manning, C.D., Garcia-Molina, H.: Clustering the tagged web. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining (2009)
Golder, S., Huberman, B.A.: The structure of collaborative tagging systems. In: Computing Research Repository (2005)
Marlow, C., Naaman, M., Davis, M., Boyd, D.: Tagging paper, taxonomy, Flickr, academic article, toread. In: Proceedings of the seventeenth ACM conference on Hypertext and hypermedia (2006)
Tatu, M., Srikanth, M., Silva, T.D.: Tag Recommendations using Bookmark Content. In: Proceeding of ECML PKDD Discovery Challenge (2008)
Lu, Y.-T., Yu, S.-I., Chang, T.-C., Hsu, J.Y.-J.: A Content-Based Method to Enhance Tag Recommendation. In: Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (2009)
Resnick, P., Iacovou, N.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the ACM conference on Computer supported cooperative work (1994)
Herlocker, J.L., Konstan, J.A., Terveen, L.G.: Konstan Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (2004)
Su, X.Y., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence (2009)
Schke, R.J., Marinho, L., Hotho, A.: Tag Recommendations in Folksonomies. In: Proceeding of ECML PKDD Discovery Challenge (2007)
Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag Recommendations Based on Tensor Dimensionality Reduction. In: Proceedings of the ACM conference on Recommender system (2008)
Hotho, A., J”aschke, R., Schmitz, C., Stumme, G.: BibSonomy: A Social Bookmark and Publication Sharing System. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, Springer, Heidelberg (2007)
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Wang, Z., Deng, Z. (2010). Tag Recommendation Based on Bayesian Principle. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_20
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DOI: https://doi.org/10.1007/978-3-642-17313-4_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17312-7
Online ISBN: 978-3-642-17313-4
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