Recon a reciprocal recommended for online dating
Many matchmaking systems require their users to assign the level of importance, referred to as weight, of a certain attribute such as age, job, and salary when they select dating partners.
However, many users do not know the exact level of importance of each attribute and thus, feel burdened to assign weights.
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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.
Also, even though users explicitly assign weights, they are often in contrast to the users’ actual behaviors in many cases.
The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China.
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