Online Dating App with Recommendation System in Python
- Partner recommendation system
- Automated dating partner matching
- Advanced location search feature
Iflexion develops an intelligent app that helps users find perfect dating partners via a Python-based recommendation engine.
Finding a perfect match isn’t easy. There are multiple parameters to assess — from personal views and education to a haircut or an eye color. The location also matters as not everyone is up to a long-distance relationship.
To make this search for perfection easier, the customer, a US-based software development company, came up with an idea of an intelligent dating application that would identify matching user profiles by calculating the probability that two users would like each other based on a range of explicit and implicit features. The system was to provide recommendations together with the matching probability.
The customer has chosen Iflexion as a trusted AI/ML services vendor to deliver the intelligent mobile solution for iOS enhanced with an advanced recommendation system in Python. The app was to create a unique user experience making it easy to search for attractive people nearby. It was also to help people get in touch and communicate with each other in a convenient and pleasant manner. But the core of the solution was to be a smart recommendation system that would suggest the user people he or she would most probably like based on his or her tastes and preferences.
Iflexion delivered an iOS app that allows the user to find potential friends and dates in the neighborhood. The recommendation system was written in Python and based on a hybrid content-collaborative model enhanced with gradient boosting.
The app’s functionality has the following key features:
- Recommendation system
- Intelligent matching algorithm
- Advanced location-based search
- Chat with rich communication features
- Highly intuitive UI
Recommendation system in Python
Iflexion’s AI developers delivered the recommendation system keeping in mind the peculiarities of online dating as compared to other domains employing recommender systems. These include:
- Availability of detailed information on each user
- Reciprocity (both parties should be satisfied with the result)
- Limited number of users, many of which have just registered
- Two possible roles a user can take (proactive and reactive)
The matchmaking algorithms were designed specifically to address the issues and leverage the advantages of online dating. They involved a combination of machine learning techniques commonly used for recommender engines, such as decision trees, collaborative filtering, and gradient boosting. The recommendation system was written in Python and used Spark for big data processing.
The recommendation system analyzes the profiles of people using the application nearby making it possible for the user to find the most suitable matches. The app calculates the matching probability, which shows how likely it is that a particular user would fancy another user. This probability is shown in the search results and recommendation panels. The system not only takes into consideration the preferences that the user has stated explicitly but also continuously extracts new implicit features from the user behavior. By comparing the predicted probability with the user's actual reaction, the algorithm constantly learns to "understand" the user better and thus starts giving more accurate predictions. The recommendation system also takes into account the preferences of similar users (i. e., those who have shown interest in the same 10 people).
The app also includes location-dependent search options that allow users to restrict the search to a certain area or distance. For this, Iflexion’s team integrated the app with the Google location services API. The location-based search and recommendations work dynamically, updating the results as the location changes.
UI and communication features
Iflexion’s professional GUI design team created an intuitive graphic user interface. They also customized some of the elements from the UIKit and Cocoa Touch frameworks to provide a really easy-to-understand and compelling UI Action-based search.
As the key functionality of the app is to allow users to chat with each other, our team implemented rich communication features. Iflexion’s UX/UI designers created a convenient and intuitive chat interface that makes the communication easy and pleasant.
The “wink” feature and a rich set of smileys and stickers add much fun to the user experience. To make sure the solution has a compelling and easy-to-understand UI, the team customized elements from the Cocoa Touch and UIKit frameworks.
Facebook and Twitter integration
Bearing in mind the popularity of such social media platforms as Facebook and Twitter, Iflexion team added the login API for them to make the solution easier to use. The users can sign in via their Facebook or Twitter account and post to these social networks via the application.
- Recommendation system: Python, Apache Spark, Nginx, Flax, PostgreSQL
- Location-based search: Google location services API
- UI: Objective-C, Cocoa Touch, UIKit
The customer successfully uploaded the app to the App Store. Welcomed by users, very soon the app earned the rating of 4.6 stars.
The solution provides iPhone owners with automatic search for other users of this dating network application and recommends best matches based on the user’s explicit and implicit preferences. The recommendation system notifies the user if someone whom they might like is found nearby and shows the matching probability.
Greatly satisfied with the professional approach to the development process demonstrated by our AI development and iOS teams, the customer also requested the development of Android and Blackberry versions of the app.
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