Abstract
Reciprocal recommendation is an important class of recommendation. It is the core of many social websites like online dating, online recruitment and so on. Different from item-to-people recommenders which only need to satisfy the preference of users, reciprocal recommenders match people and people while trying to satisfy the preferences of both parties. For each user, we provide a ranking list while trying to increase the click rate as well as the probability that clicks receiving positive replies (reciprocal interactions). Most existing methods only consider either unilateral clicks or reciprocal interactions to make recommendation. Few methods consider both of these kinds of information. In this paper, we propose a novel reciprocal recommendation method called Reciprocal-Ranking (RRK), which combines the prediction of unilateral clicks and reciprocal interactions. Experimental results on both a real-world dataset and a synthetic dataset show that RRK performs better than several state-of-the-art methods.
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Acknowledgments
This work was partially sponsored by National Key R&D Program of China (Grant No. 2017YFB10 020 02) and PKU-Tencent joint research Lab.
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Qu, Y., Liu, H., Du, Y., Wu, Z. (2018). Reciprocal Ranking: A Hybrid Ranking Algorithm for Reciprocal Recommendation. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_52
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DOI: https://doi.org/10.1007/978-3-319-97310-4_52
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