Who is dating whom: User behavior analysis and prediction for online dating sites

Online dating sites have become popular platforms for people to look for potential romantic partners. 40 million out of 50 million single people in the US have signed up with various online dating sites, and 20% of currently committed romantic relationships began online, more than through any means other than meeting through friends.

Inter-city communications of the online dating site within China
Inter-city communications of the online dating site within China

Few studies have been attempted to understand user behavior on online dating sites. In a previous work, we examined user’s online dating behavior based on a large dataset obtained from a major heterosexual online dating site in China, beihe.com. The main findings of our analysis are described as follows:

  • Many results on user messaging behavior align with notions in social and evolutionary psychology: males tend to look for younger females, while females place more emphasis on socioeconomic status, such as the income and education level.
  • Geographic distance between two users plays an important role. Out of all messages, 46.5% of them are within the same city and inter-city communications (shown in Figure 1) quickly decrease with distance.
  • Profile photos affect male and female’s behavior differently. Females with a larger number of photos are more likely to invite messages and secure replies from males, but the photo count of a male does not have as significant effect in attracting contacts and replies.
  • There is significant discrepancy between a user’s stated dating preference and his/her actual online dating behavior. Users tend to be more flexible than their online write-ups suggest.

More results can be found in our paper Who is Dating Whom: Characterizing User Behaviors of a Large Online Dating Site.

Our ICWSM 2014 paper extends our previous work to predict user’s reply behavior using a machine learning framework. Based on the profiles of the sender and receiver, as well as their prior communication traces, we seek to accurately predict whether the receiver will reply to initial contact messages from the sender, as illustrated in Figure 2. The ultimate goal is to build a reciprocal commendation system that would match users with mutual interest in each other.

Online dating interactions can be modeled as a bipartite network
Online dating interactions can be modeled as a bipartite network

We model our problem as a link prediction problem, which aims to uncover the hidden links of network. Our previous results indicate that user-based attributes are helpful to the prediction model, as a user’s message reply behavior exhibits different correlations with various user attributes including age, income, education level, geographic location, photo count, and etc. In addition to these user-based features, we also extract graph-based features from the bipartite network derived from user communication traces. A user’s message sending and replying rates reflect how actively he/she is looking for potential dates, and the message receiving rate is a good measure of the user’s popularity level. Further, we extract the interactions between users who share similar interest and attractiveness with the sender and receiver and define a set of neighbor-based features appropriate for our bipartite dating network.

Finally, we apply these features with different classification algorithms to predict whether the receiver will reply to a sender. Below are the main findings of our prediction model:

  • User-based features and graph-based features result in similar performance, and can be used for effective user reply prediction. Only a small performance gain is achieved when both feature sets are used.
  • The best performance is achieved by the random forest algorithm with precision and recall rate around 75%.
  • In user-based features, females are most concerned about age, income, house, children and parent status, which males do not show clear trend.

These studies can provide valuable guidelines to the design of recommendation engine that can match users with mutual interest in each other. More interesting results can be found in our full paper: Predicting User Replying Behavior on a Large Online Dating Site.

Peng Xia, University of Massachusetts Lowell
Hua Jiang, Baihe.com
Xiaodong Wang, Baihe.com
Cindy Chen, University of Massachusetts Lowell
Benyuan Liu, University of Massachusetts Lowell

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  • Nice work! I am curious to know how differences in availability and access to technology (and services such as online dating websites) across China show up in your data and results. Specifically, does the reciprocity rate vary as a function of whether the *recipient* is in a semi-urban and rural area? Does it matter if it was male-initiated or female-initiated?

    • Thanks for your comments. Our analysis is certainly based on users with better availability and access to technology.
      According to a recent report from The Washington Post (http://www.washingtonpost.com/blogs/the-switch/wp/2014/01/31/china-has-almost-twice-as-many-internet-users-as-the-u-s-has-people/), there are nearly 618 million Internet users in China (45.6% penetration rate) in 2013, and among these users, hundreds of millions of them have signed up with various online dating sites (http://www.onlinepersonalswatch.com/news/2013/09/jiayuancom-strong-balance-sheet-but-needing-to-reduce-user-churn.html).

      We can not differentiate urban and rural areas in our data — the user location resolution is at the city level (including its surrounding rural areas). Our results show that a large fraction (46.5%) of the messages are within same city. In general message send and reply rate quickly decreases with distance, but big cities such as Beijing, Shanghai, and Guangzhou seem to have some special attraction to females as they are more likely to send and reply to messages between these big cities.

      The gender of users has a significant effect on their online dating behavior. On average, a male sends out more messages but receives fewer messages than a female. A female is more likely to be contacted but less likely to reply to a message than a male. With respect to different user attributes, males and females show different behaviors. For example, males with higher education level or income are more attractive to females, while females with more photos are more likely to be contacted and replied to by males. You can find more details in our paper.