Design of reciprocal recommendation systems for online dating

Abstract

Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos) with a user's interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed, and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than contentbased algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious toward their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.

Social media usage as a correlate of STI risk-related sexual behavior on the college campus: Moderating effects of gender

“…Male served as the reference category for gender to which females would be compared for the models predicting sex without a condom and concurrent sex partners. Previous research suggested that men tend to be more assertive regarding online dating (Xia et al 2016). Therefore, for the model that predicted pursuing Bhookupsv ia Facebook, female served as the reference category to which male gender would be compared.…”

Who will cite you back? Reciprocal link prediction in citation networks

“…Reciprocal relationships are also explored in Bipartite social network of online dating for providing date partner recommendations (Zhao et al , 2014; Xia et al , 2016). Recently, a very interesting issue of understanding and predicting delay in reciprocal relations is investigated (Li et al , 2017).…”

Gender-specific preference in online dating

“…As a special type of social networking sites [1][2][3], online dating sites have emerged as popular platforms for single people to seek potential romance. According to a recent survey, nearly 40 million single people (out of 54 million) in the U.S. have been trying online dating, and about 20% of committed relationships began online [4]. Although some psychologists have questioned the reliability and effectiveness of online dating [5], recent empirical studies using the tracking data and survival analysis found that for heterosexual couples, meeting partners through online dating sites can speed up marriage [6].…”

“…Computer scientists usually study online dating from the perspective of user behaviors [39][40][41] and recommendation systems [4,[42][43][44]. By analyzing online dating data, Xia et al found that there exists distinct difference between preferences of men and women [41], and there also exists difference between users' stated and actual preferences.…”

“…By analyzing online dating data, Xia et al found that there exists distinct difference between preferences of men and women [41], and there also exists difference between users' stated and actual preferences. Xia et al also proposed a reciprocal recommendation system for online dating based on similarity measures [4]. For general social networks, gender differences lead to obvious differences in behaviors and preferences between men and women.…”