LaPoff, M., Chesbrough, J., Mort, L., Levy, S., MacGougan, T., McGrath, J. M., McLeod, J. (2021). System and method for providing enhanced recommendations based on ratings of offline experiences. U.S. Patent Application No. 938 A1. U.S. Patent and Trademark Office.
Maybin, S., Maguire, E., Price, H. C., Walton, J., Shah, P. (2016, e: Which dating apps are winning the hearts of the world? BBC News.
Myles, D. (2020). Romantic and sexual encounters in the age of algorithms: A comparative analysis of Grindr and Tinder. In C. Piazzesi, M. Blais, J. Lavigne, C. Lavoie Mongrain (Eds.), Contemporary intimacies and sexualities: Social changes, transformations in practices, and representations (pp. 1–11). Montreal Press.
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.
Rosenfeld, M. J., Thomas, R. J., Hausen, S. (2019). Disintermediating your friends: How online dating in the United States displaces other ways of meeting. Proceedings of the National Academy of Sciences of the United States of America, 116(36), 17753–17758.
Sharabi, L. L. (2021). Exploring how beliefs about algorithms shape (offline) success in online dating: A two-wave longitudinal investigationmunication Research, 48(7), 931–952.
Also in 2012, Hinge was founded as a dating app geared toward long-term relationships
Sharabi, L. L., Timmermans, E. (2021). Why settle when there are plenty of fish in the sea? Rusbult’s investment model applied to online dating. New Media Society, 23(10), 2926–2946.
This dating app exposes the monstrous bias of algorithms
Tong, S. T., Hancock, J. T., Slatcher, R. B. (2016). Online dating system design and relational decision making: Choice, algorithms, and control. Personal Relationships, 23(4), 645–662.
Wu, P. L., Chiou, W. B. (2009). More options lead to more searching and worse choices in finding partners for romantic relationships online: An experimental study. CyberPsychology Behavior, 12(3), 315–318.
Zhang, J., Yasseri, T. (2016). What happens after you both swipe right: A statistical description of mobile dating communications. arXiv.
©2022 Liesel L. Sharabi. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.
In 2000, eHarmony was among the first online dating sites to develop and patent a matching algorithm for pairing users with compatible partners. The eHarmony algorithm was created by a team of psychologists led by the company’s founder, Dr. Neil Clark Warren, and guided by research they conducted with 5,0). Their intention was to lower the divorce rate by applying insights from marriage to intervene in the mating ). The original eHarmony algorithm was relatively simple by today’s standards and used a regression-based approach to match users on variables believed to predict long-term relationship satisfaction (Buckwalter et al., 2004, 2008). As part of the sign-up process, users completed a compatibility test that included as many as 450 questions about themselves and their preferences for an ideal partner (eHarmony, 2021). In one of the few published tests of eHarmony’s matchmaking paradigm, couples who matched on the site had higher quality marriages than those who were introduced via ‘unfettered choice’ (Carter Buckwalter, 2009, p. 105). Of course, this does not eliminate the possibility that, algorithm aside, the eHarmony couples may have been more motivated for their relationships to succeed in the first place (Houran et al., 2004).
At Hinge, the Gale-Shapley algorithm (Gale Shapley, 1962) is used to recommend compatible matches to users (Carman, 2018). The Gale-Shapley algorithm solves the problem of creating stable matches between two groups when both sides prefer some partners over others (e.g., in the case of college admissions, marriage). Matches are stable if there are no two people who would rather be with each other than the partner they have been recommended (Gale Shapley, 1962). For instance, by matching Ravi with Ava, one can be confident that there is no one else in the dating pool they would prefer who would also be interested in them in return. Lloyd Shapley and Alvin Roth won the 2012 Nobel Memorial Prize in Economic Science for their work with the Gale-Shapley algorithm, which is in many ways a natural fit for online dating. However, this approach assumes that http://datingranking.net/es/citas-internacionales/ compatibility comes from matching people who are similar in desirability, when online daters are also known to engage in aspirational mate pursuit by seeking out the most desirable partners (Bruch Newman, 2018; Dinh et al., 2021).
Krzywicki, A., Wobcke, W., Kim, Y. S., Cai, X., Bain, M., Mahidadia, A., Compton, P. (2015). Collaborative filtering for people-to-people recommendation in online dating: Data analysis and user trial. International Journal of Human-Computer Studies, 76, 50–66.