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Role of Recommender System in Matchmaking 11 April 2022

recommender system in matchmaking


A huge number of online dating platforms have been listed over the internet. The majority of people are found browsing through these platforms and communicate frequently to find a perfect match for them. With a vast number of users, the problem of information overload arises and this might affect user experience on various platforms. Given the massive user bases of these dating platforms, there comes the need of helping users combat information overload by filtering the data and providing relevant suggestions. Otherwise, it will be hard on the path of the users to find a partner as they have to browse through and communicate with thousands of users and profiles. A recommender system can be a powerful tool for online dating platforms to help users combat this information overload and provide them with candidate recommendations according to their preferences and profile.


The popularity of the Recommender System

Nowadays the recommender system has gained immense popularity on various platforms for connecting people in a personalized method. A much higher coherent matching is accomplished with the integration of a recommender system and it has proven to convey maximum advantages for users of online matchmaking or matrimonial systems.


recommender system in matchmaking


Role of recommender system in matchmaking 

The recommender system plays an important role in the matchmaking industry. It helps a user decide on choosing a partner.  The matchmaking algorithm was designed specifically to address the problems and leverage the benefits of online dating.

The recommender system analyzes the profiles of the people using several dating platforms nearby locations and helps the user to discover the most relevant matches. The result obtained from data filtering is shown in the recommended section or panels of any dating app.

The recommendation engine not only considers the likes of the user-provided by him/her but also extracts new implicit aspects from the user behavior and thus starts giving more accurate predictions.


The major steps involved in matching profiles recommendations on the dating platforms

  • Construction of the preference model based on the messaging behavior of a given user
  • Calculation of the compatibility score with unmet users using the preference model
  • Finally, providing a list of ranked suggestions to the user based on the similar score


How different dating apps are using the recommender system?



Tinder is one of the most popular online dating and geosocial networking applications. The main purpose of Tinder is to help people meet, and establish meaningful relationships. Tinder uses a machine-learning algorithm to analyze user behavior and provide them with a better recommendation to find a perfect match for them.

Tinder’s recommender engine is programmed to collect a set of data that are tabulated accordingly to contribute a relevant output. These outputs improve the overall user experience and this is achieved by an increase in matches and messaging. 



Bumble is also using a recommender engine to significantly understand the dating preferences of the users and recommend matching profiles to them. It not only uses the user’s profile, interests and behavior but also uses the insights on several profiles that users swipe through, initiation rate of any conversation, response time to any message to recommend relevant matches. Bumble recommendation systems can understand the human heart and emotions even more, thus more effectively serving the purpose of finding the user’s the right one.


OK Cupid

OK Cupid’s recommender system calculates how much a user and a possible match would hit it off based on the choices, likes, tastes, and preferences. If a user had a similar response that matches the preferences of the other user, OK Cupid’s recommender engine matches them to help find a perfect partner.  The machine-learning algorithm uses the data to find out a couple’s compatibility and connect them and help them discover their soulmate.



Hinge is a promising dating platform that is perfectly designed to find the best match. Its recommender system works behind the screen to find the ideal partner for users. It also uses the user’s location to find potential matches in a particular area and provides a series of recommendations and thus helping users find a serious relationship rather than a casual pitch.



Recommender system for matchmaking is a powerful tool that can help several online dating platforms help their users find a perfect match easily. It has the potential of improving the value of the service to the users and thus improving monetization of the service.  


Help Your User Find a Perfect match with Alie

Alie is an AI-powered recommender system that enables a personalized user experience across websites and apps. Its unique machine learning algorithm is designed to analyze user data and recommend an ideal match on the user’s favorite online dating platform. Its built-in analytics feature helps you to track and report how your recommendations are engaging with end-users. Start a 14-days free trial to know how it can elevate user experience with personalized recommendations.


recommender system in matchmaking

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Ankit Jena
Ankit is Content Writer for Muvi’s Marketing unit. He is a passionate writer with 3+ Years of Experience in Content Creation And Development. In his past time, he likes to dance, play football and google various things to quench his thirst for knowledge.

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