  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.
