Reciprocal recommender system for online dating

Any member receives on average 3 or 4 prospective mates as selected / recommended / compatible for dating purposes per 1,000 (one thousand) members screened in the database.

They all 3 are performing the same for serious daters, with a high percentage of false positives, like gun machines shooting flowers.

I had asked Intro Analytics about the range of its algorithm.

NOW is coming "The PLAGUE of recommender systems for the Online Dating Industry""Reciprocal Recommender System for Online Dating" final version"Reciprocal Recommender System for Online Dating" demo"Learning User Preferences in Online Dating""AI Dating: Development of a Novel Dating Application with Fuzzy Inferencing"Many recommender systems do not take into account the discovery uncovered by Eastwick and Finkel 2008; also Kurzban and Weeden, 2007; Todd, Penke, Fasolo, and Lenton, 2007 who found that people often report partner preferences that are not compatible with their choices in real life.

Online dating services often use recommender systems to help people find their dates.

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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.

hat proportion of unsuitable candidates are paired with the active user.

Since presenting unsuitable candidates can be especially undesirable in this setting, the false positive rate could be the most important factor." page 2945The first recommender system (for the Online Dating Industry) I saw in a paper was:"Recommender System for Online Dating Service (2007)"then I saw the one offered by Intro Analytics.

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