Abstract
In recent years, social media services with social tagging have become tremendously popular. Because users are no longer mere consumers of content, social Web users have been overwhelmed by the huge numbers of social content available. For tailoring search results, in this paper, we look into the potential of social tagging in social media services. By leveraging collaborative filtering, we propose a new search model to enhance not only retrieval accuracy but also retrieval coverage. Our approach first computes latent preferences of users on tags from other similar users, as well as latent annotations of tags for items from other similar items. We then apply the latency of tags to a tag-based personalized ranking depending on individual users. Experimental results demonstrate the feasibility of our method for personalized searches in social media services.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
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(6), 734–749 (2005)
Bao, S., Wu, X., Fei, B., Xue, G., Su, Z., Yu, Y.: Optimizing Web Search Using Social Annotations. In: 16th International Conference on World Wide Web, pp. 501–510 (2007)
Bischoff, K., Firan, C.S., Nejdl, W., Paiu, R.: Can All Tags be Used for Search? In: 17th ACM Conference on Information and Knowledge Management, pp. 203–212 (2008)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: 14th Annual Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Cai, Y., Zhang, M., Luo, D., Ding, C., Chakravarthy, S.: Low-order Tensor Decompositions for Social Tagging Recommendation. In: 4th ACM International Conference on Web Search and Data Mining, pp. 695–704 (2011)
Deshpande, M., Karypis, G.: Item-based Top-N Recommendation Algorithms. ACM Transactions on Information Systems 22(1), 143–177 (2004)
Golder, S.A., Huberman, B.A.: Usage Patterns of Collaborative Tagging Systems. Journal of Information Science 32(2), 198–208 (2006)
Heymann, P., Koutrika, G., Garcia-Molina, H.: Can Social Bookmarking Improve Web Search? In: 1st International Conference on Web Search and Web Data Mining, pp. 195–206 (2008)
Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information Retrieval in Folksonomies: Search and Ranking. In: 3rd European Semantic Web Conference, pp. 411–426 (2006)
Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag Recommendations in Social Bookmarking Systems. AI Communications 21(4), 231–247 (2008)
Knowledge and Data Engineering Group, University of Kassel: Benchmark Folksonomy Data from BibSonomy, version of April 30th, http://www.kde.cs.uni-kassel.de/bibsonomy/dumps/
Levy, M., Sandler, M.: Learning Latent Semantic Models for Music from Social Tags. Journal of New Music Research 37(2), 137–150 (2008)
Li, X., Guo, L., Zhao, Y.E.: Tag-based Social Interest Discovery. In: 17th International Conference on World Wide Web, pp. 675–684 (2008)
Liu, F., Yu, C.T., Meng, W.: Personalized Web Search for Improving Retrieval Effectiveness. IEEE Transactions on Knowledge and Data Engineering 16(1), 28–40 (2004)
Ma, Z., Pant, G., Sheng, O.R.L.: Interest-based Personalized Search. ACM Transactions on Information Systems 25(1), Article 5 (2007)
Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., Stumme, G.: Evaluating Similarity Measures for Emergent Semantics of Social Tagging. In: 18th International Conference on World Wide Web, pp. 641–650 (2009)
Rendle, S., Schmidt-Thieme, L.: Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation. In: 3rd ACM International Conference on Web Search and Data Mining, pp. 81–90 (2010)
Shaw, J.A., Fox, E.A.: Combination of Multiple Searches. In: Text REtrieval Conference, pp. 243–252 (1993)
Vallet, D., Cantador, I., Jose, J.M.: Personalizing Web Search with Folksonomy-based User and Document Profiles. In: 32nd European Conference on Information Retrieval, pp. 420–431 (2010)
Wetzker, R., Zimmermann, C., Bauckhage, C., Albayrak, S.: I Tag, You Tag: Translating Tags for Advanced User Models. In: 3rd ACM International Conference on Web Search and Data Mining, pp. 71–80 (2010)
Wu, X., Zhang, L., Yu, Y.: Exploring Social Annotations for the Semantic Web. In: 15th International Conference on World Wide Web, pp. 417–426 (2006)
Xu, S., Bao, S., Fei, B., Su, Z., Yu, Y.: Exploring Folksonomy for Personalized Search. In: 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 155–162 (2008)
Zanardi, V., Capra, L.: Social Ranking: Uncovering Relevant Content Using Tag-based Recommender Systems. In: 2008 ACM Conference on Recommender Systems, pp. 51–58 (2008)
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
Kim, HN., Rawashdeh, M., El Saddik, A. (2011). Leveraging Collaborative Filtering to Tag-Based Personalized Search. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds) User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer Science, vol 6787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-22362-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22361-7
Online ISBN: 978-3-642-22362-4
eBook Packages: Computer ScienceComputer Science (R0)