Recommendation systems? with Dinh Thi Bui at Match
The Data Standard
Users of large online dating sites are confronted with vast numbers of candidates to browse through and communicate with. To help them in their endeavor and to cope with information overload, recommender systems can be utilized. Given the massive user bases of these dating sites, there is a need and value in helping users combat information overload by filtering the most relevant partner candidates of the abundant pool of choices. Otherwise, users have a hard time finding a partner, as they have to browse through and communicate with potentially hundreds, if not even thousands, of users and profiles. While important attributes such as age, location, gender, and relationship preference can be used to cut down on the number of viable candidates, the leftover set may still be huge especially for users living in densely populated areas. Recommender systems are a means to combat this information overload and provide users with candidate recommendations personalized to their preference and profile. Moreover, there is value in such recommender systems, even for smaller dating sites, because users attention and time can be devoted to only a handful of choices at a time and poor matches may lead to repeated rejections and discouragement. Recommender systems may also be used to reduce the burden of popular users, who may receive unruly amounts of messages, and relieve the anguish of unpopular users and users who continually get rejected by balancing the way users occur in recommendations.
Meet The Host
Data scientist at The Data Standard
Catherine Tao is a tech enthusiast looking for new methods for building connections with businesses around the world. Her extensive knowledge of data science allowed her to develop new solutions and implement them into existing ecosystems. She is currently working as a Data scientist and Exclusive Podcast Producer at The Data Standard.
Meet The Guest
Dinh Thi Bui
Vice President of New Verticals at Match