Learnings from a Retail Recommendation System on Billions of Interactions at bol.com

Abstract

Recommender systems are ubiquitous in the modern internet, where they help users find items they might like. We discuss the design of a large-scale recommender system handling billions of interactions on a European e-commerce platform. We present two studies on enhancing the predictive performance of this system with both algorithmic and systems-related approaches. First, we evaluate neural network-based approaches on proprietary data from our e-commerce platform, and confirm recent results outlining that the benefits of these methods with respect to predictive performance are limited, while they exhibit severe scalability bottlenecks. Next, we investigate the impact of a reduction of the response latency of our serving system, and conduct an A/B test on the live platform with more than 19 million user sessions, which confirms that the latency reduction of the recommender system correlates with a significant increase in business-relevant metrics. We discuss the implications of our findings with respect to real world recommendation systems and future research on scalable session-based recommendation.