Mate preferences matching outcomes online dating German bisexcouple

Posted by / 05-Dec-2019 21:56

Mate preferences matching outcomes online dating

Although the research on mate choice, both offline and online, has been extended to many fields, the following problems still exist: (i) online dating sites are a special kind of social networking sites, but the most previous researches focus only on the users’ demographic attributes, and have not considered users’ network centrality in dating sites, which can be potential important factors associated with users’ mate selection; (ii) most studies focus on male and female preferences in mate choice, but they do not properly examine the compatibility of the two parties’ preferences; (iii) with the advent of big data era, the methods of machine learning, such as ensemble learning, have been widely applied to diverse fields to achieve good prediction performance.

Specifically, when a registered member (user) feels very interested in some member, he/she will contact the member through the internal letters of the site.

For general social networks, gender differences lead to obvious differences in behaviors and preferences between men and women.

Research on an online-game society showed that females perform better economically and are less risk-taking than males, and they are also significantly different from males in managing their social networks [].

In this paper, to reveal the differences of gender-specific preference and the factors affecting potential mate choice in online dating, we analyze the users’ behavioral data of a large online dating site in China.

We find that for women, network measures of popularity and activity of the men they contact are significantly positively associated with their messaging behaviors, while for men only the network measures of popularity of the women they contact are significantly positively associated with their messaging behaviors.

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Further, we use the ensemble learning classification methods to rank the importance of factors predicting messaging behaviors, and find that the centrality indices of users are the most important factors.