AI Matching & Recommendation Engine
Get faster and more accurate matches of users, goods, services, and products.
An AI matching & recommendation engine is a tool that matches users with goods, services, and products using artificial intelligence and machine learning. To find the best match, it analyzes a variety of data, including user behavior, preferences, and interests. Its main goal is to help users find suitable options faster and more accurately, thereby improving the user experience.
AI matching engines are often confused with AI recommendation engines, but they serve different purposes. Recommendation engines provide users with personalized options based on data such as their location, purchase history, and user patterns. Matching engines, on the other hand, find the best match using resource information and advanced algorithms.
Business Value
Matching and recommendation engines are useful tools in many industries, providing a quick ROI. Matching engines automate the process of matching users with goods or services, improving existing workflows such as cold calling or attending trade shows.
Recommendation engines offer personalized services to users, using data analysis to suggest products or services tailored to their preferences. A task that would be difficult to complete accurately for a knowledge worker.
The strength of both engines is not limited by the kind of industry nor by the kind of resource you want to match with each other. Whether you are matching/recommending consumer goods with buyers or properties with companies, it will:
Increase efficiency
Boost overall efficiency by supporting the process with a data-driven approach.
Improve user experience
Enhance user experience with better matches/recommendations that require less input from the user.
Increase revenue
By increasing the number of matches that are likely to result in a transaction, you can increase revenue.
Reduce costs
Cut costs by automating the matching process, reducing manual labor and minimizing errors.
Challenges
Both AI engines (matching and recommendation) may encounter a range of functional or technical challenges, such as:
Determining the most fitting matches/recommendations
Determining the "best" matches or recommendations can be difficult because it's subjective. It may take time and customer feedback to accurately measure a matching engine's effectiveness, even with methods like A/B testing.
Determing the right data and features
To build a good recommendation engine, you need the right information. Neglecting important data leads to poor recommendations, while excessive irrelevant data hinders learning. User and behavioral data are essential for creating personalized recommendations.
Scalability
Matching engines can struggle with handling large amounts of data, especially during peak periods or when the number of products and users increases rapidly. That’s why it’s important to maintain low through scalability, optimization of speed, and caching techniques.
Integration
Integrating a matching engine with other systems, such as databases, can be difficult. It's important to make sure everything is compatible and scalable because the power of the matching engine depends on how well everything works together.
Model and data drift
A matching engine needs to be on top of changes in the real world to stay accurate. How? By adopting a strong MLOps approach (easy model monitoring, retraining, version control, ...), so the engine can continue to provide relevant matches and avoid potential losses.
Privacy and security regulations
Regulations define and sometimes limit the data that can be used in the matching process. Since many matching engines rely on user data, it's important to prioritize the privacy and security of that data, in compliance with GDPR and other relevant regulations.
The solution
A matching or recommendation engine typically consists of multiple building blocks such as:
A data layer or database combines and transforms data for the matching engine. It might use AI techniques like NLP and text extraction to represent users, goods, services, and products in a way that allows the engine to compare and match them.
The heart of the solution is the AI algorithm itself. It’s trained to provide the best possible matches or recommendations for the task at hand. It also learns from historical data and is continuously improved through a feedback mechanism that allows retraining and prevents it from becoming outdated.
Another important building block is the front-end, which displays the predicted matches to the end user. Alternatively, an API could be used to connect to downstream components that use the predictions.
Get in touch with our AI matching and recommendation engine experts
Contact us to learn how our advanced AI solutions can help you improve user experience and drive revenue growth, using state-of-the-art matching and recommendation engines powered by machine learning and data analysis. Let our experts help you find the perfect match for your business.