Abstract
Due to the higher expectation more and more online matching companies adopt recommender systems with content-based, collaborative filtering or hybrid techniques. However, these techniques focus on users explicit contact behaviors but ignore the implicit relationship among users in the network. This paper proposes a personalized social matching system for generating potential partners’ recommendations that not only exploits users’ explicit information but also utilizes implicit relationships among users. The proposed system is evaluated on the dataset collected from an online dating network. Empirical analysis shows the recommendation success rate has increased to 31% as compared to the baseline success rate of 19%.
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Nayak, R. (2011). Utilizing Past Relations and User Similarities in a Social Matching System. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20847-8_9
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DOI: https://doi.org/10.1007/978-3-642-20847-8_9
Publisher Name: Springer, Berlin, Heidelberg
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