Click-through rate (CTR) prediction is an important task for the companies to recommend products which better match user preferences. User behavior in digital advertising is dynamic and changes over time. It is crucial for the companies to capture the most recent trends to provide more accurate recommendations for users. In CTR prediction, most models use binary cross-entropy loss function. However, it does not focus on the data distribution shifts occurring over time. To address this problem, we propose a factor for the loss functions by utilizing the sequential nature of user-item interactions. This approach aims to focus on the most recent samples by penalizing them more through the loss function without forgetting the long-term information. Our solution is model-agnostic, and the temporal importance factor can be used with different loss functions. Offline experiments in both public and company datasets show that the temporal importance factor for loss functions outperforms the baseline loss functions considered.

翻译：点击率（CTR）预测是企业推荐更符合用户偏好产品的重要任务。数字广告中的用户行为具有动态性，并随时间推移而变化。企业需捕捉最新趋势以提供更精准的用户推荐，这一点至关重要。在CTR预测中，多数模型采用二元交叉熵损失函数，但其未关注随时间发生的数据分布偏移。为解决该问题，我们通过利用用户-物品交互的序列特性，提出一种损失函数因子。该方法旨在聚焦最新样本，在损失函数中对其施加更大惩罚，同时不遗忘长期信息。该方案与模型无关，时间重要性因子可适用于不同损失函数。在公开数据集与公司数据集上的离线实验表明，该时间重要性因子损失函数均优于所对比的基线损失函数。