Abstract
With the rapid growth of information on the World Wide Web, social recommendations have appeared as one of the most important roles attracting growing attentions from researchers. Social recommendations enable a form of efficient knowledge for users and help them share contents with others. There are many studies in this area focusing on using trust-relationships in recommendation algorithm, which has become a major trend in recommendation algorithms that used for searching information precisely, feasibly and efficiently, but they neglect how to build the trust-relationships framework at start. In this work, an algorithm, called PointBurst, is proposed for building a trust-relationship framework to improve the social recommendations when there is no or too few of available trust-relationships. Here, we first construct a graphical model based on a binary-type vertex relationship, where discusses the explicit and potential connections among users and recommended items. On this basis, we implement a common-used collaborative filtering recommendation algorithm to deal with the situation of enough available trust-relationships existing, and then present PointBurst, which builds trust-relationship framework as a supplement. Finally, we crawl through data from three famous recommender websites, i.e., del.icio.us, Myspace and MovieLens and use them in experiments to show that PointBurst can suggest relevant items to users’ tastes and perform better than collaborative filtering algorithm in precision and stability.
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References
Xu, Y.F., Zhang, L., Liu, W.: Cubic Analysis of Social Bookmarking for Personalized Recommendation. In: Zhou, X., Li, J., Shen, H.T., Kitsuregawa, M., Zhang, Y. (eds.) APWeb 2006. LNCS, vol. 3841, pp. 733–738. Springer, Heidelberg (2006)
Royz, S.B., Dasz, G., Yahiay, S., Yuyy, C.: Interactive Itinerary Planning. In: 2011 IEEE 27th International Conference on Data Engineering, ICDE, pp. 15–26. IEEE (2011)
Rendle, S., Gantner, Z., Freudenthaler, C., Thieme, L.: Fast Context-aware Recommendations with Factorization Machines. In: 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 331–338. ACM, New York (2011)
Smeaton, A., Callan, J.: Joint DELOS-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries. In: ACM SIGIR Forum, vol. 35. ACM, Nw York (2001)
Setten, M.V.: Experiments with a Recommendation Technique that Learns Category Interests. In: IADIS International Conference – WWW (2002)
Castellano, G., Fanelli, A.M., Torsello, M.A.: Dynamic Link Suggestion by a Neuro-Fuzzy Web Recommendation System. In: IADIS International Conference, WWW 2006, pp. 219–226. WWW (2006)
Wang, J., Li, Z.W., Yao, J.Y., Sun, Z.Q., Li, M.J., Ma, W.Y.: Adaptive User Profile Model and Collaborative Filtering for Personalized News. In: Zhou, X., Li, J., Shen, H.T., Kitsuregawa, M., Zhang, Y. (eds.) APWeb 2006. LNCS, vol. 3841, pp. 474–485. Springer, Heidelberg (2006)
Schenkel, R., Crecelius, T., Kacimi, M., Michel, S., Neumann, T., Parreira, J.X., Weikum, G.: Efficient Top-k Querying over Social-Tagging Networks. In: 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 523–530. ACM, New York (2008)
Golbeck, J.: Generating Predictive Movie Recommendations from Trust in Social Networks. In: Stølen, K., Winsborough, W.H., Martinelli, F., Massacci, F. (eds.) iTrust 2006. LNCS, vol. 3986, pp. 93–104. Springer, Heidelberg (2006)
Machanavajjhala, A., Korolova, A., Sarma, A.D.: Personalized Social Recommendations Accurate or Private? In: VLDB Endowment, vol. 4. ACM, New York (2011)
Assent, I.: Actively building private recommender networks for evolving reliable relationships. In: 25th International Conference on Data Engineering, pp. 1611–1614. IEEE (2009)
Li, C.T., Lin, S.D., Shan, M.S.: Exploiting Endorsement Information and Social Influence for Item Recommendation. In: 34th International ACM SIGIR Conference on Research and Development in Information, SIGIR 2011, pp. 1131–1132. ACM, New York (2011)
Ma, H., King, I., Lyu, M.: Learning to Recommend with Social Trust Ensemble. In: 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 203–210. ACM, New York (2009)
Konstas, I.: On Social Networks and Collaborative Recommendation. In: 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, pp. 195–202. ACM, New York (2009)
Cho, J.H., Kwon, K.: Source Credibility Model for Neighbor Selection in Collaborative Web Content Recommendation. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds.) APWeb 2008. LNCS, vol. 4976, pp. 68–80. Springer, Heidelberg (2008)
Dupret, G., Piwowarski, B.: A User Browsing Model to Predict Search Engine Click Data from Past Observations. In: 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 331–338. ACM, New York (2011)
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Wu, H., Wang, X., Peng, Z., Li, Q., Lin, L. (2012). PointBurst: Towards a Trust-Relationship Framework for Improved Social Recommendations. In: Wang, H., et al. Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29426-6_11
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DOI: https://doi.org/10.1007/978-3-642-29426-6_11
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