Reciprocal recommender system for online dating

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However, making that trade-off decision is something that warrants future research, as it is not clear how different criteria affect user experience and likelihood of finding a partner in a live online dating context.ABSTRACT One important class of recommender system involves people as both the subject and object of the recommendation. Some examples are: employment web sites, which help a job seeker and employer and the right employer nd each other; dating web sites; mentor-mentee matching systems.They all 3 are performing the same for serious daters, with a high percentage of false positives, like gun machines shooting flowers.That range convergence phenomenon is what I had called in 2003, when I had discovered than problem, 7 long years ago.There is a range convergence phenomenon between the 3 mains tools online dating sites can offer: searching by your own, Bidirectional Recommendation Engines and Compatibility Matching Methods.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.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.Some online dating sites had been using Behavioural Bidirectional Recommendation Engines for years, like Plenty Of Fish, and they could not outperform compatibility Matching Methods based on personality profiling.

We conclude with a discussion, linking our work in online dating to the many other domains that require reciprocal recommenders.

We present a new recommender system for online dating.

Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people.

This analysis provides the foundation for our Content-Collaborative Reciprocal (CCR) recommender approach.

The content-based part uses selected user profile features and similarity measure to generate a set of similar users.

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