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.

翻译：本文引入上下文预测模型的概念。该模型仅依赖用户特征与上下文特征（不包含任何具体物品特征）来确定用户行为（如点击或转化）的概率。我们发现了该建模方法的多种实用应用场景，包括训练辅助上下文模型来估计点击概率，并将其预测结果作为特征集成到点击率预估模型中。实验表明，这种增强方式在离线与在线业务指标上均有显著提升，同时对服务成本的影响极小。总体而言，我们提出了一种简单、可扩展且高效的方法，用于提升大规模商业推荐系统的性能，对个性化推荐领域具有广泛的指导意义。