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.

引入了 Context-Based Prediction Models 这一概念，通过仅依赖用户和环境特征，而不考虑物品特定特征的方式来预测用户的行为概率，该方法在许多有价值的应用中表现出显著优势，包括训练辅助的上下文模型以估计点击概率，并将其预测作为 CTR 预测模型的一个特征进行加入，实验证明这种改进在离线和在线业务指标上带来了显著的提升，而对服务成本几乎没有影响，总体上，我们的工作为大规模商业推荐系统的性能提升提供了一种简单且可扩展却强大的方法，对个性化推荐领域具有广泛的影响。