Overview
The Automated Content Curation and Recommendation Platform is a system designed to personalize content delivery to users based on their interests and preferences. Unlike traditional recommendation systems, it uses a user-to-topic mapping approach, ensuring that users receive content relevant to their interests, avoiding the pitfalls of echo chambers or popularity-driven recommendations. The system aggregates content from various sources using APIs, processes it efficiently, and delivers personalized recommendations in real-time. Its architecture is scalable, using technologies like Kafka, Redis, and Kubernetes for efficient data handling and fast content delivery.
Tech Stack
One of the primary challenges in developing this platform was ensuring scalability while maintaining performance. With the integration of multiple external content sources (News API, Reddit API, YouTube API), real-time data ingestion became a significant bottleneck. Handling the massive influx of data through Kafka and ensuring that the microservices hosted on Kubernetes could process and categorize this data in real time required fine-tuning of the system. Another key challenge was managing the real-time personalized content recommendations, which needed to balance speed with accuracy. Ensuring that the caching system (Redis) didn’t experience memory constraints during high load periods was crucial for maintaining performance. The user-to-topic mapping system had to be meticulously designed to ensure that recommendations were not driven by user behavior but were based purely on their stated interests, which posed unique challenges in model development and system architecture.
The Challenge
One of the primary challenges in developing this platform was ensuring scalability while maintaining performance. With the integration of multiple external content sources (News API, Reddit API, YouTube API), real-time data ingestion became a significant bottleneck. Handling the massive influx of data through Kafka and ensuring that the microservices hosted on Kubernetes could process and categorize this data in real time required fine-tuning of the system. Another key challenge was managing the real-time personalized content recommendations, which needed to balance speed with accuracy. Ensuring that the caching system (Redis) didn’t experience memory constraints during high load periods was crucial for maintaining performance. The user-to-topic mapping system had to be meticulously designed to ensure that recommendations were not driven by user behavior but were based purely on their stated interests, which posed unique challenges in model development and system architecture.
The Solution
To address these challenges, we implemented a robust system architecture using Kafka for real-time data ingestion, which decoupled the data collection process from content processing. Kafka acted as a message broker, ensuring that the system could handle high-throughput data streams efficiently. We utilized Kubernetes to host microservices, ensuring that the system could scale horizontally as the load increased, with automatic scaling and load balancing. Redis was used to cache user-specific feeds and preferences, reducing the need for repeated feed generation and improving the system’s response times. For content categorization and recommendation generation, we implemented BERTTopic-based topic modeling, which helped align content with users’ defined interests. Additionally, MongoDB stored user profiles and recommendation history, enabling the system to evolve and improve the accuracy of recommendations over time. We also integrated Prometheus and Grafana for real-time monitoring and alerts, allowing us to quickly detect and respond to system performance issues, such as Kafka consumer lag or Redis memory exhaustion. These strategies ensured the system could handle high traffic volumes while maintaining low latency and high user satisfaction.
The Result
The project successfully built an LLM-powered news feed that curates personalized content using vector databases to store user preferences. Feed generation was optimized with Kubernetes, Kafka, and real-time embeddings, resulting in a 45% reduction in load times. The integration of Prometheus and Grafana allowed for effective observability, enabling proactive tuning and improving system reliability. The platform delivered a seamless user experience, providing tailored, relevant content in real-time while maintaining high scalability and performance.
