Recommender systems with cascading architecture play an increasingly significant role in online recommendation platforms, where the approach to dealing with negative feedback is a vital issue. For instance, in short video platforms, users tend to quickly slip away from candidates that they feel aversive, and recommender systems are expected to receive these explicit negative feedbacks and make adjustments to avoid these recommendations. Considering recency effect in memories, we propose a forgetting model based on Ebbinghaus Forgetting Curve to cope with negative feedback. In addition, we introduce a Pareto optimization solver to guarantee a better trade-off between recency and model performance. In conclusion, we propose Pareto-based Multi-Objective Recommender System with forgetting curve (PMORS), which can be applied to any multi-objective recommendation and show sufficiently superiority when facing explicit negative feedback. We have conducted evaluations of PMORS and achieved favorable outcomes in short-video scenarios on both public dataset and industrial dataset. After being deployed on an online short video platform named WeChat Channels in May, 2023, PMORS has not only demonstrated promising results for both consistency and recency but also achieved an improvement of up to +1.45% GMV.

翻译：级联架构的推荐系统在在线推荐平台中发挥着日益重要的作用，其中负反馈处理策略是核心问题。例如在短视频平台中，用户倾向于快速划过厌恶的候选内容，推荐系统需接收这些显式负反馈并动态调整以避免同类推荐。考虑到记忆的近因效应，我们提出基于艾宾浩斯遗忘曲线的遗忘模型来应对负反馈。此外，引入帕累托优化求解器以保障近因效应与模型性能间的更优平衡。最终，我们提出基于帕累托最优与遗忘曲线的多目标推荐系统（PMORS），该方法可适用于任意多目标推荐场景，并在处理显式负反馈时展现出显著优势。我们在公开数据集与工业数据集上对PMORS进行了短视频场景评估，均取得优异效果。自2023年5月部署于微信视频号在线平台后，PMORS不仅在一致性与近因性方面表现优异，更实现GMV提升最高达+1.45%。