Abstract
To deal with the tri-relation of user-resource-tag in folksonomies and the data sparsity problem in personalized recommendation, we propose a user taste diffusion model based on the tripartite hypergraph to recommend resources for users. Through the defined tri-relation model and diffusion probability matrix, the user’s taste is diffused from itself to other users, resources and tags. When diffusion stops, the candidate resources can be identified then be ranked according to the taste values. As a result the top resources that have not been collected by the given user are selected as the final recommendations. Benefiting from the introduction of iterative diffusion mechanism, the recommendation results not only cover the resources collected by the given user’s direct neighbors but also cover the ones which are collected by his/her extended neighbors. Experimental results show that our method performs better in terms of precision and recall than other recommendation methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 734–749 (2005)
Zhou, T., Ren, J., Medo, M., Zhang, Y.C.: Bipartite network projection and personal recommendation. Physical Review E 76, 1–7 (2007)
Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems 22, 116–142 (2004)
Yeung, C.A., Gibbins, N., Shadbolt, N.: A Study of User Profile Generation from Folksonomies. In: Proceedings of the Social Web and Knowledge Management Workshop, Beijing (2008)
Niwa, S., Doi, T., Honiden, S.: Web page recommender system based on folksonomy mining. In: Proceedings of the Third International Conference on Information Technology, Las Vegas, pp. 388–393 (2006)
Tso-Sutter, K.H.L., Mariho, L.B., Schmidt-Thieme, L.: Tag-aware recommender systems by fusion of collaborative filtering algorithms. In: Proceedings of the 2008 ACM Symposium on Applied Computing, Fortaleza, pp. 1995–1999 (2008)
Peng, J., Zeng, D., Zhao, H., Wang, F.: Collaborative filtering in social tagging systems based on joint item-tag recommendations. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, pp. 809–818 (2010)
Liang, H., Xu, Y., Li, Y., Nayak, R.: Collaborative filtering recommender systems using tag information. In: Proceedings of Web Intelligence/Intelligent Agent Technology Workshops, Sydney, pp. 59–62 (2008)
Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L., Jäschke, R., Hotho, A., Stumme, G., Symeonidis, P.: Social tagging recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 615–644. Springer, Berlin (2011)
Zhang, Y.C., Medo, M., Ren, J., Zhou, T., Li, T., Yang, F.: Recommendation model based on opinion diffusion. Europhysics Letters 80, 1–5 (2007)
Liu, J.G., Wang, B.H., Guo, Q.: Improved collaborative filtering algorithm via information transformation. International Journal of Modern Physics C 20, 285–293 (2009)
Zhang, Z.K., Zhou, T., Zhang, Y.C.: Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Physica A 389, 179–186 (2010)
Gourab, G., Vinko, Z., Guido, C., Newman, M.E.J.: Random hypergraphs and their applications. Physical Review E 9, 1–10 (2009)
Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18, 613–620 (1975)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22, 5–53 (2004)
Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag Recommendations in Folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)
Herlocker, J.L., Konstan, J.A., Borehers, A., Riedl, J.T.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proceedings of the 22nd ACM Conference on Research and Development in Information Retrieval, Berkeley, pp. 230–237 (1999)
Halpin, H., Robu, V., Shepherd, H.: The Complex Dynamics of Collaborative Tagging. In: Proceedings of the 16th International Conference on World Wide Web, Banff, pp. 211–220 (2007)
Millen, D.R., Feinberg, J.: Using Social Tagging to Improve Social Navigation. In: Workshop on the Social Navigation and Community based Adaptation Technologies, Dublin (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, J., Shi, Y., Guo, C. (2011). A Resource Recommendation Method Based on User Taste Diffusion Model in Folksonomies. In: Xiong, H., Lee, W.B. (eds) Knowledge Science, Engineering and Management. KSEM 2011. Lecture Notes in Computer Science(), vol 7091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25975-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-25975-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25974-6
Online ISBN: 978-3-642-25975-3
eBook Packages: Computer ScienceComputer Science (R0)