Kun Tu
David Jensen
ABSTRACT
Recommendation systems for online dating have recently attracted much attention from the research community. In this paper we propose a two-side matching framework for online dating recommendations and design an Latent Dirichlet Allocation (LDA) model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large online dating website shows that two-sided matching improves the rate of successful matches by as much as 45%. Finally, using simulated matching, we show that the LDA model can correctly capture user preferences.
- H. Adachi. A search model of two-sided matching under nontransferable utility. Journal of Economic Theory, 113(2):182--198, Dec. 2003.Google Scholar
Cross Ref
- S. Alsaleh, R. Nayak, Y. Xu, and L. Chen. Improving Matching Process in Social Network Using Implicit and Explicit User Information. In APWeb, 2011. Google ScholarDigital Library
- K. J. Archer and R. V. Kimes. Empirical characterization of random forest variable importance measures. Computational Statistics & Data Analysis, 52(4):2249--2260, 2008. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3:993--1022, 2003. Google ScholarDigital Library
- L. Brozovsky and V. Petricek. Recommender system for online dating service. arXiv preprint cs/0703042, 2007.Google Scholar
- X. Cai, M. Bain, A. Krzywicki, W. Wobcke, Y.S. Kim, P. Compton, and A. Mahidadia. Learning collaborative filtering and its application to people to people recommendation in social networks. In ICDM, 2010. Google ScholarDigital Library
- L. Chen, R. Nayak, and Y. Xu. Improving Matching Process in Social Network. In ICDMW, pages 305--311. IEEE, 2010. Google ScholarDigital Library
- F. Diaz, D. Metzler, and S. Amer-Yahia. Relevance and ranking in online dating systems. In SIGIR, 2010. Google ScholarDigital Library
- T. G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine learning, 40(2):139--157, 2000. Google ScholarDigital Library
- D. Gale and L. Shapley. College admissions and the stability of marriage. American Mathematical Monthly, 69(1):9--15, 1962.Google ScholarCross Ref
- G. J. Hitsch, A. Hortacsu, and D. Ariely. Matching and Sorting in Online Dating. The American Economic Review, 100(1):130--163, Jan. 2010.Google ScholarCross Ref
- F. P. Kelly, A. K. Maulloo, and D. K. Tan. Rate control for communication networks: shadow prices, proportional fairness and stability. Journal of the Operational Research society, 49(3):237--252, 1998.Google ScholarCross Ref
- R. Kohavi. Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. In SIGKDD, 1996.Google Scholar
- A. Krzywicki, W. Wobcke, X. Cai, A. Mahidadia, M. Bain, P. Compton, and Y. S. Kim. Interaction-based collaborative filtering methods for recommendation in online dating. In WISE. 2010. Google ScholarDigital Library
- S. Kutty, R. Nayak, and L. Chen. A people-to-people matching system using graph mining techniques. World Wide Web, pages 1--39, 2013. Google ScholarDigital Library
- H. Liu and R. Setiono. Chi2: Feature selection and discretization of numeric attributes. In ICTAI, 1995. Google ScholarDigital Library
- R. Nayak, M. Zhang, and L. Chen. A Social Matching System for an Online Dating Network: A Preliminary Study. In ICDMW, 2010. Google ScholarDigital Library
- L. Pizzato, T. Rej, T. Chung, I. Koprinska, and J. Kay. RECON: A reciprocal recommender for online dating. In RecSys, 2010. Google ScholarDigital Library
- A. Roth and M. Sotomayor. Two-sided matching: A study in game-theoretic modeling and analysis. Cambridge University Press, 1992.Google Scholar
- D. Slater. Love in the Time of Algorithms. Penguin Group, 2013.Google Scholar
- P. Xia, B. Ribeiro, C. Chen, B. Liu, and D. Towsley. A study of user behavior on an online dating site. In ASONAM, 2013. Google ScholarDigital Library
- K. A. Zweig and M. Kaufmann. A systematic approach to the one-mode projection of bipartite graphs. Social Network Analysis and Mining, 1(3):187--218, 2011.Google ScholarCross Ref
Index Terms
- Online dating recommendations: matching markets and learning preferences
Recommendations
An effective recommendation method for cold start new users using trust and distrust networks
Recommendation systems analyze the purchasing behavior (e.g., item ratings) of users to learn about their preferences and recommend products or services that may be of interest to them. However, as new users require time to become familiar with ...
A contextual-bandit approach for multifaceted reciprocal recommendations in online dating
AbstractRecommender Systems (RS) provide an effective way to deal with the problem of information overload by suggesting relevant items to users that the users may prefer. However, many online social platforms such as online dating and online recruitment ...
Reciprocal recommender system for online dating
RecSys '10: Proceedings of the fourth ACM conference on Recommender systemsReciprocal recommender is a class of recommender systems that is important for tasks where people are both the subject and the object of the recommendation; one such task is online dating. We have implemented RECON, a reciprocal recommender for online ...
Comments