Multi-task optimization is an emerging research field in recommender systems that focuses on improving the recommendation performance of multiple tasks. Various methods have been proposed in the past to address task weight balancing, gradient conflict resolution, Pareto optimality, etc, yielding promising results in specific contexts. However, when it comes to real-world scenarios involving user sequential behaviors, these methods are not well suited. To address this gap, we propose AcouRec, a novel and effective approach for sequential behavior modeling in multi-task recommender systems inspired by acoustic attenuation. Specifically, AcouRec introduces an impact attenuation mechanism to mitigate the uncertain task interference in multi-task optimization. Extensive experiments on public datasets demonstrate the effectiveness of AcouRec.