Abstract
In a typical social network site, a sender initiates an interaction by sending a message to a recipient, and the recipient can decide whether or not to send a positive or negative reply. Typically a sender tries to find recipients based on his/her likings, and hopes that they will respond positively. We examined historical data from a large commercial social network site, and discovered that a baseline success rate using such a traditional approach was only 16.7%. In this paper, we propose and evaluate a new recommendation method that considers a sender’s interest, along with the interest of recipients in the sender while generating recommendations. The method uses user profiles of senders and recipients, along with past data on historical interactions. The method uses a weighted harmonic mean-based aggregation function to integrate “interest of senders” and “interest of recipients in the sender”. We evaluated the method on datasets from the social network site, and the results are very promising (improvement of up to 36% in success rate).
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References
Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Balabanović, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Communications of the ACM 40(3), 66–72 (1997)
Linden, G., Smith, B., York, J.: Amazon.Com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Deshpande, M., Karypis, G.: Item-Based Top-N Recommendation Algorithms. ACM Transactions on Information Systems (TOIS) 22(1), 143–177 (2004)
Karypis, G.: Evaluation of Item-Based Top-N Recommendation Algorithms. In: Tenth International Conference on Information and Knowledge Management, pp. 247–254 (2001)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-Based Collaborative Filtering Recommendation Algorithms. In: 10th International Conference on World Wide Web, pp. 285–295 (2001)
Huang, Y., Contractor, N., Yao, Y.: Ci-Know: Recommendation Based on Social Networks. In: 9th Annual International Digital Government Research Conference, pp. 375–376 (2008)
Mobasher, B., Burke, R., Bhaumik, R.: Attacks and Remedies in Collaborative Recommendation. IEEE Intelligent Systems 22(3), 56–63 (2007)
Palau, J., Montaner, M., de la Rosa, J.L.: Collaboration Analysis in Recommender Systems Using Social Networks. In: The Third International Joint Conference on Autonomous Agents and Multi Agent Systems, pp. 137–151 (2004)
Perugini, S., Gonçalves, M.A., Fox, E.A.: Recommender Systems Research: A Connection-Centric Survey. Journal of Intelligent Information Systems 23(2), 107–143 (2004)
Figueira, J.R., Greco, S., Ehrgott, M.: Multiple Criteria Decision Analysis: State of the Art Surveys. International Series in Operations Research & Management Science, vol. 78 (2005)
Adomavicius, G., Kwon, Y.: New Recommendation Techniques for Multicriteria Rating Systems. IEEE Intelligent Systems 22(3), 48–55 (2007)
Lee, H.-H., Teng, W.-G.: Incorporating Multi-Criteria Ratings in Recommendation Systems. In: IEEE International Conference on Information Reuse and Integration (IRI 2007), pp. 273–278 (2007)
Rattanajitbanjong, N., Maneeroj, S.: Multi Criteria Pseudo Rating and Multidimensional User Profile for Movie Recommender System. In: 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009, pp. 596–601 (2009)
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Kim, Y.S. et al. (2010). People Recommendation Based on Aggregated Bidirectional Intentions in Social Network Site. In: Kang, BH., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2010. Lecture Notes in Computer Science(), vol 6232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15037-1_21
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DOI: https://doi.org/10.1007/978-3-642-15037-1_21
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