When Online Dating Meets Nash Social Welfare: Achieving Efficiency and Fairness

Mobile dating applications such as Coffee Meets Bagel, Tantan, and Tinder, have become significant for young adults to meet new friends and discover romantic relationships. From a system designer's perspective, in order to achieve better user experience in these applications, we should take both the efficiency and fairness of a dating market into consideration, so as to increase the overall satisfaction for all users. Towards this goal, we investigate the nature of diminishing marginal returns for online dating markets (i.e., captured by the submodularity), and trade-off between the efficiency and fairness of the market with Nash social welfare. We further design effective online algorithms to the apps. We verify our models and algorithms through sound theoretical analysis and empirical studies by using real data and show that our algorithms can significantly improve the ecosystems of the online dating applications.

[1]  Hamed Haddadi,et al.  A first look at user activity on tinder , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[2]  R SumterSindy,et al.  Love me Tinder , 2017 .

[3]  M. Keith Chen,et al.  Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform , 2016, EC.

[4]  Daniel Lehmann,et al.  Combinatorial auctions with decreasing marginal utilities , 2001, EC '01.

[5]  Jon Feldman,et al.  Online Stochastic Packing Applied to Display Ad Allocation , 2010, ESA.

[6]  Günter J. Hitsch,et al.  Matching and Sorting in Online Dating , 2008 .

[7]  Janelle Ward,et al.  What are you doing on Tinder? Impression management on a matchmaking mobile app , 2017 .

[8]  Laura Vandenbosch,et al.  Love me Tinder: Untangling emerging adults' motivations for using the dating application Tinder , 2017, Telematics Informatics.

[9]  Peter Buxmann,et al.  Gender Differences in Online Dating: What Do We Know So Far? A Systematic Literature Review , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[10]  E. Eisenberg,et al.  CONSENSUS OF SUBJECTIVE PROBABILITIES: THE PARI-MUTUEL METHOD, , 1959 .

[11]  Hervé Moulin,et al.  Fair division and collective welfare , 2003 .

[12]  Wei Xu,et al.  An Optimization Framework for Online Ride-Sharing Markets , 2016, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[13]  Ashutosh Sabharwal,et al.  An Axiomatic Theory of Fairness in Network Resource Allocation , 2009, 2010 Proceedings IEEE INFOCOM.

[14]  Richard Cole,et al.  Approximating the Nash Social Welfare with Indivisible Items , 2018, SIAM J. Comput..

[15]  Yan Wang,et al.  Measuring Education Inequality: Gini Coefficients of Education , 1999 .

[16]  Jennie Zhang,et al.  What Happens After You Both Swipe Right: A Statistical Description of Mobile Dating Communications , 2016, ArXiv.

[17]  Rohit Vaish,et al.  Finding Fair and Efficient Allocations , 2017, EC.

[18]  J. Nash.  THE BARGAINING PROBLEM , 1950, Classics in Game Theory.

[19]  Jan Vondrák,et al.  Optimal approximation for the submodular welfare problem in the value oracle model , 2008, STOC.

[20]  Ulrich Endriss,et al.  Nash Social Welfare in Multiagent Resource Allocation , 2009, AMEC/TADA.

[21]  Jan Vondrák,et al.  Online Submodular Welfare Maximization: Greedy is Optimal , 2012, SODA.

[22]  Gábor Orosz,et al.  Too many swipes for today: The development of the Problematic Tinder Use Scale (PTUS) , 2016, Journal of behavioral addictions.

[23]  Yan Wang,et al.  Measuring Education Inequality: Gini Coefficients of Education. Policy Research Working Paper. , 2001 .

[24]  Tadayoshi Kohno,et al.  How Public Is My Private Life?: Privacy in Online Dating , 2017, WWW.

[25]  Aranyak Mehta,et al.  Online Matching and Ad Allocation , 2013, Found. Trends Theor. Comput. Sci..

[26]  Martin Hoefer,et al.  Approximating the Nash Social Welfare with Budget-Additive Valuations , 2017, SODA.

[27]  Euiwoong Lee,et al.  APX-hardness of maximizing Nash social welfare with indivisible items , 2015, Inf. Process. Lett..

[28]  Christopher J. Carpenter,et al.  The players of micro-dating: Individual and gender differences in goal orientations toward micro-dating apps , 2016, First Monday.

[29]  Jean-Charles Rochet,et al.  Two-Sided Markets: An Overview ∗ , 2004 .

[30]  Dan Ariely,et al.  What makes you click?—Mate preferences in online dating , 2010 .

[31]  Jessica Strubel,et al.  Love me Tinder: Body image and psychosocial functioning among men and women. , 2017, Body image.

[32]  Vahab S. Mirrokni,et al.  Tight information-theoretic lower bounds for welfare maximization in combinatorial auctions , 2008, EC '08.

[33]  Yuval Filmus,et al.  A Tight Combinatorial Algorithm for Submodular Maximization Subject to a Matroid Constraint , 2012, 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science.

[34]  Aranyak Mehta,et al.  Inapproximability Results for Combinatorial Auctions with Submodular Utility Functions , 2005, Algorithmica.

[35]  Morteza Zadimoghaddam,et al.  Online Submodular Welfare Maximization: Greedy Beats 1/2 in Random Order , 2015, STOC.

[36]  Adam Wierman,et al.  Prices and Subsidies in the Sharing Economy , 2016, WWW.

[37]  Simina Brânzei,et al.  Nash Social Welfare Approximation for Strategic Agents , 2016, EC.

[38]  Ariel D. Procaccia,et al.  The Unreasonable Fairness of Maximum Nash Welfare , 2016, EC.