Abstract
Due to the rapid development of the Internet and need for recommendation systems, there have been several recommendation systems using the various information on the Internet and more and more systems are using the SNS information. However, most of them only consider the simple direct friend relationships. In this paper we use the intimacy and similarity between users on the SNS to compute the weight of an evaluating person for recommendation purpose. The intimacy between users considers the direct and distant friend relationships on an SNS which contains direction and importance information among friends. The similarity between users is computed by using the mutual friends as well as the relationship between the user’s preference and the given item. In order to enhance the objectivity among user’s evaluations, the evaluation was performed on several item attributes. We have used real SNS data to carry out experiments and show how well the intimacy and similarity can predict the target user’s evaluation ratings.
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References
Bonhard, P., Sasse, M.A.: Knowing me, knowing you — using profiles and social networking to improve recommender systems. BT Technology and Journal 24(3) (2006)
Ma, H., Zhou, D., Liu, C.: Recommender Systems with Social Regularization. In: Proceedings of the fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 287–296 (2011)
Rong, Y., Wen, X., Cheng, H.: A Monte Carlo algorithm for cold start recommendation. In: Proc. WWW, pp. 327–336 (2014)
Khanh, Q.T., Ishikawa, F., Honiden, S.: Improving Accuracy of Recommender System by Clustering Items Based on Stability of User Similarity. In: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce, CIMCA 2006, p. 61 (2006)
He, J., Chu, W.W.: A Social Network-Based Recommender System (SNRS). Data Mining for Social Network Data Annals of Information Systems 12, 47–74 (2010)
Seol, K.-S., Kim, J.-D., Shim, H.-N.: Doo-Kwon Baik: Intimacy Measurement Method and Experiment between Social Network Service Users. Journal of KIISE: Information Networking 39(04), 335–341 (2012)
Haveliwalla, T.H.: Topic-Sensitive PageRank. In: Proceedings of the 11th International World Wide Web Conference, pp. 517–526 (2002)
Guy, I., Jacovi, M., Perer, A., Ronen, I., Uziel, E.: Same Places, Same Things, Same People? Mining User Similarity on Social Media. In: Proc. of the 2010 ACM Conference on Computer Supported Cooperative Work, pp. 41–50 (2010)
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Kim, Y., Lee, M. (2015). An Approach for Applying Social Networks Information to Information Recommendation. In: Park, J., Stojmenovic, I., Jeong, H., Yi, G. (eds) Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol 330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45402-2_182
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DOI: https://doi.org/10.1007/978-3-662-45402-2_182
Publisher Name: Springer, Berlin, Heidelberg
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