Reciprocal recommendation is the task of finding preferable matches among users in two distinct groups. Popular examples of reciprocal recommendation include online job recruiting and online dating services. In this paper, we propose a new method of reciprocal recommendation that uses a graph embedding technique. In particular, we use cross-domain matching correlation analysis (CDMCA) as a graph embedding method. In CDMCA, feature vectors in different domains are mapped into a common representation space, and reciprocal recommendation is conducted in the common mapped space. Numerical experiments show that the CDMCA with a similarity-based weighting scheme provides a high-quality reciprocal recommendation.
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Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L.A.S, Kay, J., & Rej, T. (2011). CCR—A content-collaborative reciprocal recommender for online dating. In T. Walsh (Ed.), IJCAI, IJCAI/AAAI (pp. 2199–2204).
Brun, A., Castagnos, S., & Boyer, A. (2011). Social recommendations: Mentor and leader detection to alleviate the cold-start problem in collaborative filtering. In T. P. H. I-Hsien Ting & L. S. Wang LS (Eds.), Social network mining, analysis and research trends: Techniques and applications, IGI global (pp. 270–290).
Cai, X., Bain, M., Krzywicki, A., Wobcke, W., Kim, Y.S., Compton, P., & Mahidadia, A. (2010). Collaborative filtering for people to people recommendation in social networks. In J. Li (Ed.) AI 2010: Advances in Artificial Intelligence. AI 2010. Proceedings of the Australian Joint Conference on Artificial Intelligence, LNCS (Vol. 6464, pp. 476–485). Berlin: Springer.
Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27:1–27:27. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Fukui, K., Okuno, A., & Shimodaira, H. (2016). Image and tag retrieval by leveraging image-group links with multi-domain graph embedding. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 221–225).
Gao, C., Ma, Z., Zhang, A. Y., & Zhou, H. H. (2017). Achieving optimal misclassification proportion in stochastic block models. Journal of Machine Learning Research, 18, 1–45.
Hong, W., Zheng, S., Wang, H., & Shi, J. (2013). A job recommender system based on user clustering. Journal of Computers, 8(8), 1960–1967.
Hopcroft, J., Lou, T., & Tang, J. (2011). Who will follow you back?: Reciprocal relationship prediction. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11 (pp. 1137–1146). New York: ACM.
Huang, Z., Shan, S., Zhang, H., Lao, S., & Chen, X. (2012). Cross-view graph embedding. In Computer Vision ACCV (pp. 770–781).
Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 20(4), 422–446.
Jeh, G., & Widom. J. (2002). Simrank: A measure of structural-context similarity. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’02 (pp. 538–543). New York: ACM.
Kim, Y. S., Krzywicki, A., Wobcke, W., Mahidadia, A., Compton, P., Cai, X., & Bain, M. (2012). Hybrid techniques to address cold start problems for people to people recommendation in social networks. In PRICAI 2012: Trends in Artificial Intelligence—12th Pacific Rim International Conference on Artificial Intelligence, Kuching, Malaysia, September 3–7, 2012. Proceedings (pp. 206–217).
Kishida, K. (2005). Property of average precision as performance measure for retrieval experiment. Tech. rep., National Institute of Informatics, nII-2005-014E.
Leskovec, J., Huttenlocher, D., & Kleinberg, J. (2010). Predicting positive and negative links in online social networks. In Proceedings of the 19th International Conference on World Wide Web, WWW ’10 (pp. 641–650). New York: ACM.
Li, L., & Li, T. (2012). MEET: A generalized framework for reciprocal recommender systems. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM ’12 (pp. 35–44). New York: ACM.
Nori, N., Bollegala, D., & Kashima, H. (2012). Multinomial relation prediction in social data: A dimension reduction approach. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, July 22–26, 2012. Toronto, Ontario, Canada.
Pizzato, L., Rej, T., Chung, T., Koprinska, I., & Kay, J. (2010). RECON: A reciprocal recommender for online dating. In Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys ’10 (pp. 207–214). New York: ACM.
Pizzato, L., Rej, T., Akehurst, J., Koprinska, I., Yacef, K., & Kay, J. (2013). Recommending people to people: The nature of reciprocal recommenders with a case study in online dating. User Modeling and User-Adapted Interaction, 23(5), 447–488.
R Core Team. (2017). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. https://www.R-project.org/.
Real, R., & Vargas, J. M. (1996). The probabilistic basis of Jaccard’s index of similarity. Systematic Biology, 45(3), 380–385.
Rohe, K., Chatterjee, S., & Yu, B. (2011). Spectral clustering and the high-dimensional stochastic blockmodel. The Annals of Statistics, 39(4), 1878–1915.
Saibaba, A. K., Lee, J., & Kitanidis, P. K. (2015). Randomized algorithms for generalized Hermitian eigenvalue problems with application to computing Karhunen–Loève expansion. Numerical Linear Algebra with Applications, 23, 314–339.
Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web, LNCS (Vol. 4321, pp. 291–324). Berlin: Springer.
Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels. Cambridge, MA: MIT Press.
Schütze, H., Manning, C. D., & Raghavan, P. (2008). Introduction to information retrieval (Vol. 39). Cambridge: Cambridge University Press.
Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. New York, NY: Cambridge University Press.
Shimodaira, H. (2015). A simple coding for cross-domain matching with dimension reduction via spectral graph embedding. arXiv:1412.8380.
Tu, K., Ribeiro, B., Jensen, D., Towsley, D., Liu, B., Jiang, H., & Wang, X. (2014). Online dating recommendations: Matching markets and learning preferences. InProceedings of the 23rd International Conference on World Wide Web, WWW ’14 Companion (pp. 787–792). New York: ACM.
Wang, C., Han, J., Jia, Y., Tang, J., Zhang, D., Yu, Y., & Guo, J. (2010). Mining advisor-advisee relationships from research publication networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10 (pp. 203–212). New York: ACM.
Williams, C. K. I., & Seeger, M. (2001). Using the nyström method to speed up kernel machines. In T. K. Leen, T. G. Dietterich, V. Tresp (Eds.), Advances in neural information processing systems (Vol. 13, pp. 682–688). MIT Press.
Xia, P., Jiang, H., Wang, X., Chen, C., & Liu, B. (2014). Predicting user replying behavior on a large online dating site. In International AAAI Conference on Web and Social Media.
Xia, P., Liu, B., Sun, Y., & Chen, C. (2015). Reciprocal recommendation system for online dating. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ACM, ASONAM ’15 (pp. 234–241).
Yu, H., Liu, C., & Zhang, F. (2011). Reciprocal recommendation algorithm for the field of recruitment. Journal of Information & Computational Science, 8(16), 4061–4068.
Yu, M., Zhao, K., Yen, J., & Kreager, D. (2013). Recommendation in reciprocal and bipartite social networks-a case study of online dating. In Social Computing, Behavioral-Cultural Modeling and Prediction—6th International Conference, SBP 2013, Washington, DC, USA, April 2–5, 2013. Proceedings (pp. 231–239).
TK was supported by KAKENHI 16K00044, 15H03636, and 15H01678.
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Sudo, K., Osugi, N. & Kanamori, T. Numerical study of reciprocal recommendation with domain matching. Jpn J Stat Data Sci 2, 221–240 (2019). https://doi.org/10.1007/s42081-019-00033-3
- Reciprocal recommendation
- Cross-domain matching correlation analysis
- Jaccard similarity