Recommender system for online dating service

Posted by / 29-Dec-2017 09:47

Recommender system for online dating service

The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms.Results taken a few months after deployment show that the recommender system delivered its projected benefits.Skilled employees learn a lot about their customers’ preferences simply by observing their behavior, understanding their current needs and recalling past purchases.Recently, there has been a resurgence in this sort of highly personalized service in stores, and online retailers are dealing with the problem by applying technology.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.This model is then used to predict items (or ratings for items) that the user may have an interest in.

A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed, and the recommendation list is generated to include users with top scores.Online dating is a prime application area for recommender systems, as users face an abundance of choice, must act on limited information, and are participating in a competitive matching market.This article reports on the successful deployment of a people-to-people recommender system on a large commercial online dating site.Not only is available in more than 25 countries and eight languages — but it also has more than 30 million members, sees more than 13.5 million visitors a month, and is responsible for more romantic connections than any other dating site.In our opinion, these are the reasons Match is the best online destination for international dating.

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Abstract: The aim of the thesis is to research the utility of collaborative filtering based recommender systems in the area of dating services.

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