Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models

TL;DR

This paper introduces Context-Based Prediction Models that improve large-scale recommender systems by relying solely on user and contextual features, leading to significant performance gains with minimal additional cost.

Contribution

The work presents a novel, scalable approach for enhancing recommender systems using context-based models that do not depend on item features.

Findings

Significant improvements in offline metrics

Enhanced online business metrics

Minimal increase in serving costs

Abstract

In this work, we introduce the notion of Context-Based Prediction Models. A Context-Based Prediction Model determines the probability of a user's action (such as a click or a conversion) solely by relying on user and contextual features, without considering any specific features of the item itself. We have identified numerous valuable applications for this modeling approach, including training an auxiliary context-based model to estimate click probability and incorporating its prediction as a feature in CTR prediction models. Our experiments indicate that this enhancement brings significant improvements in offline and online business metrics while having minimal impact on the cost of serving. Overall, our work offers a simple and scalable, yet powerful approach for enhancing the performance of large-scale commercial recommender systems, with broad implications for the field of personalized recommendations.