Online dating research paper conclusion recommendation
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 content-based algorithms in both precision and recall.
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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.
Of these relationships formed, on average they lasted for approx 7 months, with 18% of them lasting for over a year.
Such figures are in line with relationships formed in more traditional ways.