Machine Learning for Dating Apps
Nowadays there are a lot of dating apps to help people to find their significant others. Some dating apps are quite popular among young people also there are some dating apps especially for the elderly. The purpose of those apps is to filter potential matches based on the users’ personal preferences, such as height, degree, habit, region, and occupation, etc.
Fraud Detection
For dating apps, there is a potential risk — scam! In order to protect the users and provide them better services, fraud detection is an important part of app development. One part of the job for backend developers (data scientists, and machine learning engineers, etc.) is to apply for machine learning with the user’s behavior data to detect any suspicious activities.
So it could be double-layer protection. The first layer is to do some verifications to make sure the new users are “true”! For example, image recognition can be used for dating apps and for “forget password” function. Image recognition is an image classification and normally it comes with PCA (Principal Component Analysis) which is to reduce the dimensions to speed up a machine learning algorithm.
Besides, when new users register, they can log in with their emails or other accounts such as Google, Facebook, Apple, Emails, and users’ bio-information such as name, age, job, phone, date of birth, and so on. For the second layer protection, machine learning or deep learning algorithms could be applied. When some “bad people” have some suspicious behaviors, the system should send a warning message to other users who are “dating ” with those “bad people”. Old school machine learning algorithms are basically to learn from patterns of users’ normal behaviors which is very efficient and it can quickly distinguish suspicious activities before users encounter fraud. On the other hand, Deep Learning can look at suspicious signals such as how many females a male browses, and determine whether he is copying and pasting messages for many girls, or whether he mentions something about money, investment, credit card, and private information.
User Behavior Data
Every user is different and unique. In order to offer customized service and matches, tracking their behavior data is a core thing. Then there will be some questions that can be answered
- What is the app conversion rate? How to improve it?
- What is the geographic data?
- What kind of soul mates or partners that female users’ who are aged 20–25 are looking for?
- What the most popular “occupation” or “age”?
- How those users make decisions, etc.
Those datasets are valuable and those are what our machine needs to learn and to find more actionable insights!
Clustering
One possible ML for dating apps is Clustering Analysis — unsupervised classification! For example, we want to know if males prefer females who are 5 years younger than them or some young guys prefer females who are 3 years older than them. Clustering can help us to answer those questions and find more interesting insights, which can help dating apps to improve user experience.
NLP (Natural Language Processing)
For some dating apps, there is a Q&A section for users. In this section, there are some interesting questions that users need to answer, such as
- What is your hobit?
- Do you have a pet /pets?
- What do you do in your leisure time?
We can use NLP based on those text data and combine them with classification algorithms to generate more accurate matches.
Besides, NLP also could be used in more different aspects such as — what kind of topics are hot, and what groups of people are most popular.
Recommendation System
Obviously, the apps will offer you so many different matches based on your preferences. However, sometimes you think that all the recommendations that the system gave you cannot meet your needs or cannot satisfied with you. The recommendations system is a good choice for dating apps. Not only it can recommend some matches according to your basic preferences but also we can analyze the user behavior data to offer users more accurate and customized matches.
Of course, there are ‘filter’ and ‘sort’ functions that users can use, however, some users do not exactly know what kind of soulmates they want. Therefore the recommendation system plays a key role and improves user experience.
*Or maybe we can use Game Theory to provide users optimal strategies so that the system can satisfy most users’ expectations. For more details of Game Theory, please visit my previous blog.
Conclusion
Approximately 70% of the couples will meet online in recent years, and dating applications have become more and more widely used. Machine Learning algorithms can help those apps to serve their users better and improve some functions also well.
