Data Science and Dating Apps: A Case Study
Introduction
Algorithmic matchmaking is used in online dating. Most applications will ask you a series of questions or require you to list your preferences, the responses to which will be evaluated by an algorithm and used to match you with possible companions. It's essentially a gamification of social interaction. There are a number of drawbacks to using (such as safety, objectification, superficiality, and so on), but there are also advantages.
To some extent, the applications also believe that 'love' is quantifiable. Love has patterns, and these algorithms use them to promote compatible mates throughout the network.
Popularity & Revenue of Dating Apps
Apps like Bumble, Hinge, Tinder, and others use a freemium model, in which the app's main capabilities are free, but premium extras are available via subscription or one-time purchase. Match Group's primary emphasis is undoubtedly Tinder, with a 123% 5Y Revenue CAGR, but the business has also made significant investments in Hinge.
As many conventional places to meet people (bars, gyms, etc.) have closed due to the epidemic, many people have turned to apps. People are also willing to pay for more match chances, as seen by the increase in Average Revenue per User to $0.60. Hinge's user base has expanded tenfold in the last three years, with a +60% growth in ARPU year over year, indicating that customers are prepared to pay for matches.
Algorithm Behind Dating Apps
Following are some of the algorithms (data science applications, in particular) that dating apps employ:
- When users join up for a dating service, they are asked to complete a personality assessment, some of which can be hundreds of lengthy questions. However, while the responses give sites with a wealth of important personal information about visitors, it is not the only information they utilise.
With users' consent, many applications and sites obtain extra data insight from other sites that users visit, such as social media platforms, streaming site choices, and even online purchasing histories. This type of data may disclose a lot about a person, and dating services frequently use it to adjust for the fact that, even if they don't realise it, many users lie in their questionnaire responses.
- This approach, known as collaborative filtering, matches users based on characteristics such as their most-watched shows and the kind of things they purchase. It can provide more harmonic matches than questionnaire data alone, particularly when people are inclined to look more desirable on paper by concealing their genuine likes and dislikes. This is where Facebook's dating app is expected to shine, given it has access to years of honest and impartial personal preference data.
Taking social media data a step further, dating apps provide users with a thorough portrait of their personality when they link it to their Twitter account. On their users' Twitter feeds, they employ a type of artificial intelligence known as 'natural language processing' (NLP) to draw inferences about them, score their compatibility with others, and give dating recommendations and advice.
- Dating apps also employ the Gale-Shapley algorithm that links people "who are likely to mutually enjoy one another". It calculates this based on your interaction and who engages with you, and it connects you with individuals who share your interests. The dating market is bifurcated: one individual seeks out another, and the platform facilitates connection. It is mostly based on network effects: the greater the pool from which the app draws, the more likely it is to identify someone who matches tastes.
This leads to the 'Stable Marriage Problem,' which seeks a stable marriage between two entities given their preferences. Assume there are four bugs and four trees. We need to use stable pairings to match the problem to a tree. Stable does not imply ideal – not everyone will be fully content with their spouse, but they would not prefer anybody else who is available over their existing companion (Pareto-optimal).
- Deep learning allows an app to learn to recognise certain facial traits by analysing large quantities of photos of real faces. A deep learning application that can train itself may identify the important features of a face that it needs to recognise to separate one person from another, such as the shape of the nose or the colour of the eyes, without being taught. When a user uploads an image of the type of person they wish to meet, the app searches its database for people who have traits that are most similar to the person in the original image. Badoo, a relative newcomer to the dating app sector, is one such app.
Conclusion
It's hardly surprising that much of the data analytics underpinning online dating aims to find what users are actually like and what they're truly seeking for, given the very human desire to idealise oneself to others when on the hunt for love. Data Science course in Delhi and Big Data can teach us a lot about consumers based on their online behaviour, whether they are dating app users, present or future customers or clients. Learning Data Science in Kolkata so as to employ them in real-time is also a task nowadays, especially in a world where even dating is dependent upon it. Skillslash courses may be useful in this sense. It helps students gain Real Work Experience, which involves working on real projects for start-ups and global organisations, as well as earning direct certification from IT firms for their work experience.
