Abstract
With the explosive growth of social communities, users of social Web systems have experienced considerable difficulty with discovering communities relevant to their interests. In this paper we address the problem of recommending communities (or groups) to individual users. We regard this problem as tag-based personalized searches. Based on social tags used by members of communities, we first represent communities in a low-dimensional space, the so-called latent semantic space, by using Latent Semantic Analysis. Then, for recommending communities to a given user, we capture how each community is relevant to both that user’s personal tag usage and other community members’ tagging patterns in the latent space. Our evaluation on the CiteULike dataset shows that our approach can significantly improve the recommendation quality.
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References
Baatarjav, E.-A., Phithakkitnukoon, S., Dantu, R.: Group Recommendation System for Facebook. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM-WS 2008. LNCS, vol. 5333, pp. 211–219. Springer, Heidelberg (2008)
Berry, M.W., Dumais, S.T., O’Brien, G.W.: Using Linear Algebra for Intelligent Information Retrieval. SIAM Review 37(4), 573–595 (1995)
Chen, W.-Y., Zhang, D., Chang, E.Y.: Combinational Collaborative Filtering for Personalized Community Recommendation. In: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 115–123 (2008)
Chen, W.-Y., Chu, J.-C., Luan, J., Bai, H., Wang, Y., Chang, E.Y.: Collaborative Filtering for Orkut Communities: Discovery of User Latent Behavior. In: 18th International Conference on World Wide Web, pp. 681–690 (2009)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41(6), 391–407 (1990)
Deshpande, M., Karypis, G.: Item-based Top-N Recommendation Algorithms. ACM Transactions on Information Systems 22(1), 143–177 (2004)
Lee, D.H., Brusilovsky, P.: Interest Similarity of Group Members: The Case Study of Citeulike. In: The WebSci 2010: Extending the Frontiers of Society On-Line (2010)
Li, X., Guo, L., Zhao, Y.E.: Tag-based Social Interest Discovery. In: 17th International Conference on World Wide Web, pp. 675–684 (2008)
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)
Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42 (2007)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of Dimensionality Reduction in Recommender System–A Case Study. In: ACM WebKDD 2000 Web Mining for E-Commerce Workshop (2000)
Vallet, D., Cantador, I., Jose, J.M.: Personalizing Web Search with Folksonomy-Based User and Document Profiles. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 420–431. Springer, Heidelberg (2010)
Vasuki, V., Natarajan, N., Lu, Z., Savas, B., Dhillon, I.: Scalable Affiliation Recommendation using Auxiliary Networks. ACM Transactions on Intelligent Systems and Technology 3(1), Article 3 (2011)
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)
Zheng, N., Li, Q., Liao, S., Zhang, L.: Which Photo Groups Should I Choose? A Comparative Study of Recommendation Algorithms in Flickr. Journal of Information Science 36(6), 733–750 (2010)
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Akther, A., Kim, HN., Rawashdeh, M., El Saddik, A. (2012). Applying Latent Semantic Analysis to Tag-Based Community Recommendations. In: Kosseim, L., Inkpen, D. (eds) Advances in Artificial Intelligence. Canadian AI 2012. Lecture Notes in Computer Science(), vol 7310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30353-1_1
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DOI: https://doi.org/10.1007/978-3-642-30353-1_1
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