Ever start a new show thanks to your Netflix recommended list?
A recommendation engine is a powerful tool that utilizes artificial intelligence and data analytics to provide personalized recommendations to users. It analyzes user behaviour, preferences, and historical data to suggest relevant products, content, or actions, enhancing the user experience and driving sales or engagement.
How does a recommendation engine work?
Step 1: data collection
Step 2: data storage
Step 3: data analysis
Step 4: data filtering
3 main types of techniques of recommendation systems:
- Content-based filtering – based on single user interaction and preference
- Collaborative filtering – collecting info from interactions of many users to make suggestions (wider taste)
- Knowledge-based system – based in influence of user needs
They can adapt and learn from user interactions in real-time, continually refining and improving the recommendations provided. Additionally, they can leverage data from various sources, including explicit feedback from users (ratings, reviews) and implicit signals (click-through rates, time spent on pages), to further enhance the relevance of recommendations.
Recommendation engines efficiency is measured by:
→ Accuracy: corrects recommendations out of the total possible recommendations.
→ Coverage measures: number of items the system is able to provide recommendations for.
Recommendation engines enhance the user experience, increase sales, and drive engagement by offering relevant and tailored suggestions. As technology continues to advance, recommendation engines will play an even more vital role in assisting users and businesses in navigating the vast amount of information and choices available.