Abstract
Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an alternative, trust can be used for neighbor formation to generate automated recommendation. User assigned explicit trust rating such as how much they trust each other is used for this purpose. However, reliable explicit trust data is not always available. In this paper we propose a new method of developing trust networks based on user’s interest similarity in the absence of explicit trust data. To identify the interest similarity, we have used user’s personalized tagging information. This trust network can be used to find the neighbors to make automated recommendations. Our experiment result shows that the proposed trust based method outperforms the traditional collaborative filtering approach which uses users rating data. Its performance improves even further when we utilize trust propagation techniques to broaden the range of neighborhood.
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References
Park, S.T., Pennock, D., Good, N., Decoste, D.: Naïve Filterbots for Robust Cold-start Recommendations. In: Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA (2006)
Massa, P., Avesani, P.: Trust-aware Recommender Systems. In: ACM Recommender Systems Conference, Minneapolis, MN, USA (2007)
Ziegler, C.N., Golbeck, J.: Investigating interactions of trust and interest similarity. Decision Support Systems 43(2), 460–475 (2007)
Bhuiyan, T.: A Survey on the Relationship between Trust and Interest Similarity in Online Social Networks. To appear in the Journal of Emerging Technologies in Web Intelligence 3 (2010)
Golbeck, J.: Computing and Applying Trust in Web-based Social Networks. PhD thesis, University of Maryland College Park (2005)
Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extentions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)
Sarwar, B.M., Karypis, G., Konstan, J.A., Reidl, J.: Analysis of Recommendation algorithms for e-commerce. In: The Second ACM Conference on Electronic Commerce, Minneapolis, Minnesota, USA, pp. 158–167 (2000)
Fu, B.: Trust Management in Online Social Networks. MSc Dissertation, Trinity College, University of Dublin, Ireland (2007)
Tso-Sutter, K.H.L., Marinho, L.B., Schmidt-Thieme, L.: Tag-awer Recommender Systems by Fusion of Collaborative Filtering Algorithms. In: Proceedings of the ACM Symposium on Applied Computing, USA, pp. 1995–1999 (2008)
Liang, H., Xu, Y., Li, Y., Nayak, R., Weng, L.T.: Personalized Recommender Systems Integrating Social Tags and Item Taxonomy. In: Proceedings of the Joint Conference on Web Intelligence and Intelligent Agent Technology, Italy, pp. 540–547 (2009)
Liang, H., Xu, Y., Li, Y., Nayak, R.: Collaborative Filtering Recommender Systems based on Popular Tags. In: Proceedings of the 14th Australasian Document Computing Symposium, Sydney (2009)
Heymann, P., Ramage, D., Garcia-Molina, H.: Social Tag Prediction. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Informational Retrieval, USA, pp. 531–538 (2008)
Gemmis, M.D., Lops, P., Semeraro, G., Basile, P.: Integrating Tags in a Semantic Content-based Recommender. In: Proceedings of ACM Conference on Recommender Systems 2008, pp. 163–170 (2008)
Sen, S., Vig, J., Riedl, J.: Tagomenders: Connecting Users to Items through Tags. In: Proceedings of WWW 2009, pp. 671–680 (2009)
Cleverdon, C.W., Mills, J., Keen, M.: Factors Determining the Performance of Indexing Systems. ASLIB Cranfield Project, Cranfield (1966)
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)
Jøsang, A., Hayward, R., Pope, S.: Trust Network Analysis with Subjective Logic. In: Proceedings of the 29th Australasian Computer Science Conference, CRPIT, Hobart, Australia, vol. 48 (2006)
Jøsang, A., Bhuiyan, T.: Optimal Trust Network Analysis with Subjective Logic. In: The Second International Conference on Emerging Security Information, Systems and Technologies, Cap Esterel, France (2008)
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Bhuiyan, T., Xu, Y., Jøsang, A., Liang, H., Cox, C. (2010). Developing Trust Networks Based on User Tagging Information for Recommendation Making. In: Chen, L., Triantafillou, P., Suel, T. (eds) Web Information Systems Engineering – WISE 2010. WISE 2010. Lecture Notes in Computer Science, vol 6488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17616-6_32
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DOI: https://doi.org/10.1007/978-3-642-17616-6_32
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