Deep learning for bespoke recommender systems
Exploring the Power of Deep Learning in Optimising User Experience in Dating Apps
Matchmaking with deep learning: recommender systems for dating
Whether finding a product online or seeking a partner for dating, the role of recommender systems stretches over various domains, playing a critical role in our decision-making process. Let’s delve in and see how these systems revolutionise matchmaking in the dating industry.
What is a Recommender System?
Online retailers, such as Amazon, often suggest ‘similar products’ once you make a purchase. These suggestions arise from an area of machine learning known as recommender systems.
The standard approach for these recommender systems is filling matrices with information about various products and analysing the relationships between them. If you’ve noticed, most products you buy often have similar products recommended alongside, usually those that go together in the same basket quite often.
Recommender Systems for Dating — the Challenge
Switch the focus to a dating website, and things get tricky. While it’s simple to suggest products based on previous purchases — as is the case with online retailers, recommending a partner is not as straightforward. There are countless users, each unique and with different preferences.
We usually have data on:
- User’s profile text
- Profile photo
- Contact requests (if any).
With this information, we use a deep learning technique called vector embeddings to make recommendations.
How it Works
- By using a system, each profile text is converted into a ‘fingerprint’ or a vector in a 100-dimensional space.
- While individually the 100-dimensional vector holds little meaning, similar tastes typically result in similar vectors.
- To recommend potential partners to a new user, the system calculates their vector, determines the distances to other vectors, and finds the nearest neighbours!
Broadening the Horizon: Other Industry Applications
These text-based recommender systems are not only for dating apps. They are useful in other sectors like:
- Recruitment websites: Applicants upload their CVs, and the platform suggests relevant jobs.
- Real-estate platforms: Property descriptions and photos are used for recommendations.
But bear in mind that while there are off-the-shelf recommender systems for retail or movie suggestions, image-based or text-based recommendations require highly customized solutions, considering the complexity and specificity of data.
At Fast Data Science, we offer consulting services after decades of learning from experience and dealing with machine learning and natural language data. If you have abundant text or image data and seek an advanced recommender system, we’d love to hear from you. Learn more here or leave a comment below.
