In today’s content-rich digital world, delivering the right content to the right user at the right time is crucial. Building on our earlier work on Momspresso’s data pipeline, we’ve now implemented a powerful recommendation engine that personalizes content for millions of Momspresso users. Let’s dive into how we built this system.
The Challenge#
Momspresso needed a recommendation system that could:
- Process large volumes of user interaction data
- Generate personalized article recommendations quickly
- Update recommendations in real-time as users interact with content
- Scale to handle millions of users and articles
Our Solution: A Spark-Powered Recommendation Engine#
We designed a multi-component recommendation system that leverages the data pipeline we built earlier:
1. Data Generation Scripts#
Using the event store from our data pipeline, we created scripts to generate the training set for our recommendation model. This allows us to use real user interaction data to train our model.
2. Spark ML-lib for Model Training#
We set up a Spark ML-lib based system for model training. We’re currently using collaborative filtering, which can be trained quickly with just 3-4 days of data. This allows us to update our model frequently, ensuring our recommendations stay relevant.
3. Recommendation Web Service#
We built a web service that serves article recommendations based on user IDs. To address the high latency of loading the model into memory, we implemented a caching strategy using Redis. This ensures quick response times for our recommendations.
4. Delete Recommendation Service#
To keep recommendations fresh, we implemented a service that removes viewed articles from a user’s recommendations. This service connects to Kafka and listens for view events, updating the recommendations in real-time.
Key Features of Our Recommendation Engine#
Personalization: By using collaborative filtering, we can provide tailored recommendations based on similar users’ behaviors.
Real-time Updates: Our system updates recommendations as users interact with content, ensuring relevance.
Scalability: The use of Spark and Redis allows our system to handle large volumes of data and users efficiently.
Flexibility: Our modular design allows us to easily swap out the recommendation algorithm or add new features in the future.
Implementation and Results#
Integrating the recommendation engine with Momspresso’s platform was straightforward. We made a small configuration change in Nginx to use our new recommendation web service as the API for one of the feeds on the production website.
Early results have been promising:
- Increased Engagement: Users are spending more time on the platform, reading more articles per session.
- Improved Discovery: Users are finding and engaging with a wider variety of content.
- Enhanced User Satisfaction: Initial feedback suggests users find the personalized recommendations valuable.
Looking Ahead#
As we continue to refine our recommendation engine, we’re excited about several future enhancements:
- Multi-model Approach: Implementing different recommendation models for different types of content or user segments.
- Content-based Filtering: Incorporating article features to improve recommendations, especially for new or niche content.
- A/B Testing Framework: Building a system to easily test different recommendation strategies.
By continually improving our recommendation engine, we’re helping Momspresso deliver more value to their users, keeping them engaged and coming back for more personalized content.
Stay tuned for our next post, where we’ll discuss how we’re using the data pipeline and recommendation engine to derive actionable insights for Momspresso’s content strategy!