Online dating consumer preferences
To address the research gap, in this paper, using empirical data from a large online dating site in China, we explore the users’ attribute preference compared with random selection, and use logistic regression to study how the users’ demographic attributes, popularity and activity and compatibility scores are associated with messaging behaviors, which reveal the gender differences in potential mate selection.
We also use ensemble learning classifiers to sort the importance of various potential factors predicting messaging behaviors.
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.
Finally, by correlation analysis we find that men and women show different strategic behaviors when sending messages.
Compared with men, for women sending messages, there is a stronger positive correlation between the centrality indices of women and men, and more women tend to send messages to people more popular than themselves.
These results have implications for understanding gender-specific preference in online dating further and designing better recommendation engines for potential dates.
There are total 548,395 users in the dataset including 344,552 male users and 203,843 female users.
For general social networks, gender differences lead to obvious differences in behaviors and preferences between men and women.