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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.It explores the mid-twentieth century origins of computer dating and matchmaking in order to argue for the importance of using sexuality as a lens of analysis in the history of computing.Doing so makes more visible the heteronormativity that silently structures much of our technological infrastructure and helps bring other questions about gender, race, and class into the foreground.We address this problem with a two-stage recommender process that employs a Decision Tree derived from interactions data as a “critic” to re-rank candidates generated by Collaborative Filtering.Our baseline Profile Matching method dynamically chooses, for each user, attributes that contribute most significantly to successful interactions with candidates having the best matching attribute value. Just ask Mark Brooks, who has worked in and around the industry for two decades. It's estimated that 15% of Americans have used dating websites or apps, with numbers expected to rise in the next many years. Marketers have played a huge role in helping this industry grow from simple online personals to complex platforms with millions of users. New innovations and a growing user base now give marketers more data than ever to play with in online dating. No website has found the magic method of creating a love connection.

Our methods were first evaluated on historical data from a large online dating site and then trialled live over a 9 week period providing recommendations via e-mail to a large number of users.In this paper, we study Collaborative Filtering for people-to-people recommendation in online dating, comparing this approach to a baseline Profile Matching method.Initial data analysis highlights the problem of over-recommending popular users, a standard problem for Collaborative Filtering applied to product recommendation, but more acute in people-to-people recommendation.Abstract: Although online dating has only recently become culturally acceptable and widespread, using computers to make romantic matches has a long history.But rather than revolutionizing how people met and married, this article shows how early computerized dating systems re-inscribed conservative social norms about gender, race, class, and sexuality.

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