Online dating overview (and Bumble)
As access to the Internet and mobile devices became increasingly prevalent across the globe in the last 20 years, online dating has become widely popular, socially accepted, and even essential for many urban professionals. Bumble, one of the new comers in the industry, operates similarly to Tinder where users will indicate their preferences for other users’ profile by swiping either to the left or to the right. The difference is that only female members can initiate conversations after matching, leading the “feminist movement” in the dating apps scene. 
The online dating industry amounts to 2.9 billion USD last year, and it is estimated that the current players only capture as little as 10% of singles worldwide, which I believe serve as a strong indicator of its potential growth.  As many have experiences, while online dating opened up the pool of candidates for chatting and dating, it has also created a platform for many disappointing experiences- both when the app is not correctly understanding your preference and sending you the matches you would liked, or when other members on the app are not acting respectfully, which causes users to drop out and become disillusioned with the idea of the online dating. This is where Machine Learning comes to play.
Machines make the best matchmakers
In the short term, in order to grow and retain users, the competitive landscape of the online dating industry is posing two important questions to Bumble. The first is to to make better matches and recommendations. Secondly, Bumble needs to better protect its community values on the platform by weeding out users who are disrespectful of others.
Some dating apps have already used big data to help users dynamically display their profile photo based on the number of “right swipes” to help maximize their chance of getting matches.  In my opinion, these improvements are tactical and short term focused and only scratches the surface of what Machine Learning can achieve. With Machine Learning technology, Bumble is able to significantly better understand your dating preference, not only through the profiles everyone create and the “interests” that you indicate, but also by digging out the implications and insights through a plethora of members’ mobile “fingerprints” by reading your swipe pattern, initiation rates of certain conversation, response time to messages. Because of the amount data that Bumble obtains, as well as the increasing processing speed of machine, Bumble has the potential of understanding your human heart and emotions even more than you do yourself, hence more efficiently serving the purpose of finding you the ”one.“
However, the ability for Bumble to capitalize on Machine Learning to improve its matching algorithm is much contingent on the size of the network and the amount of interactive data it obtains. Therefore, Bumble needs to better address issues with its customer experiences so that they can continuously grow its user base. Many users dropped out of Bumble after experiencing verbal abuse from other members. By design, because Bumble only allows female users to initiate conversations, the app is already filtering out many unwelcome messages that jeopardizes users experiences and causes user churn. However, the problem is not eradicated. Bumble can leverage Machine Learning capability to better understand the behavioral patterns from users. By understanding and verifying good behaviors, solely based on user’s interactive data on the platform, such as whether someone swipes judiciously or responds to messages appropriately, the system can more effectively predict and reward those that would help maintain the trustworthiness of the platform, hence building a virtuous cycle for scaling its network. 
In the long term, when Machine Learning technology is being developed, Bumble would need to focus even more on user’s privacy protection. Research has shown that users of online dating apps are generally more concerned about institutional privacy safety (social media companies selling personal data to third parties) than social privacy (others users see your information).  When machines can understand more about users preferences and the complexities of individual users’ sexuality expressions, companies need to do more about disclosing the privacy information to users and actively enforcing on strict procedural and technical methods to prevent these hyper sensitive information from being unlawfully extracted and revealed.
- What is the maximize capability for machines to capture the complexity of human sexual and emotional attraction? Research has indicated that machines, even after fully trained with some data, are not very good at predicting human attraction in experimental settings .
- As social media giant Facebook is also getting in the online dating real, how can Bumble and alikes fend off the competition where its competitor has 185 million daily active users in US and Canada alone.  Is Facebook’s entry an immediate threat to Bumble? Or is Facebook’s entry more of a industry wide validation?
- Charlotte Alter, “Whitney Wolfe Wants To Beat Tinder At Its Own Game,” Time, Http://Time.Com/3851583/Bumble-whitney-wolfe/, May 2015
- Leigh Gallagher, “Match Is The Sweetheart Of Online Dating—but Can It Fend Off Facebook And Bumble?”, Fortune Http://Fortune.Com/2018/06/27/Match-dating-tinder-facebook-bumble/, June 2018
- Ananya Bhattacharya, “Tinder Is A/B Testing Your Face”, Quartz, Https://Qz.Com/809681/Tinders-machine-learning-algorithms-can-now-serve-your-most-appealing-photos-to-potential-dates/,October 2016
- Christoph Lutz And Giulia Ranzini, “Where Dating Meets Data: Investigating Social And Institutional Privacy Concerns On Tinder”, Social Media + Society, January 2017
- Samantha Joe, Paul W. Eastwick, And Eli J. Finkel, L, “Is Romantic Desire Predictable? Machine Learning Applied To Initial Romantic Attraction”, Psychological Science, July 2017
- Kurt Wagner And Rani Molla, Facebook’s User Growth Has Hit A Wall, Recode, Https://Www.Recode.Net/2018/7/25/17614426/Facebook-fb-earnings-q2-2018-user-growth-troubles, July 2018
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