Building adaptive recommendation systems

A diverse team of professionals working together in an office with laptops and a wheelchair user. Photo: Ivan S/Pexels

Adaptive recommenders are complex systems that learn and evolve continuously based on user behavior. The hardest part of building these systems is not creating a model, but rather designing the entire system to operate under real-world constraints such as latency, cost, and customer trust.

Recommendation systems have been dealing with challenges like feedback loops, evaluation, and online learning for years, and the lessons learned from these systems are becoming broadly applicable as AI becomes more adaptive. Adaptive systems are not just machine learning models, but rather a system that continuously changes based on observed interactions.

Building Adaptive Systems

Classical recommendation systems operated in a relatively static way, training models periodically and refreshing features periodically. In contrast, adaptive systems learn continuously and quickly, ingesting signals and incorporating context in real-time.

Adaptive systems are different from static systems in that they make decisions based on current user behavior, rather than relying on yesterday’s understanding of the world. This requires a different approach to system design, with a focus on latency, freshness, and adaptability.

Retrieval and Ranking

Retrieval is a critical stage in recommendation systems, as it determines what items are considered for ranking.

Ranking is also an important stage, as it estimates the value of each item and determines what is shown to the user. However, ranking is not just about model complexity, but also about signal complexity and combining different types of signals.

Orchestration is a key aspect of adaptive systems, as it involves coordinating multiple components and making decisions in real-time. This requires a deep understanding of user behavior and intent, as well as the ability to balance multiple objectives and optimize for different metrics.

Case Study: Adaptive Homepage Systems

Adaptive homepage systems are a challenging problem, as they need to balance multiple conflicting goals such as relevance, freshness, diversity, and engagement. These systems need to adapt to changing user intent and behavior, and make decisions in real-time.

Freshness is a critical factor in adaptive systems, as users expect to see up-to-date and relevant content. A simpler model with fresher information can outperform a more complex model with stale information.

Evaluation is also a key aspect of adaptive systems, as it requires defining metrics that align with product goals and user behavior. Multi-objective evaluation is necessary, as different metrics can conflict with each other.

Migration and organizational scaling are also important considerations, as adaptive systems require alignment between different teams and stakeholders. Success depends on organizational alignment and the ability to make trade-offs between different objectives.