Along with the exponential growth of online platforms and services, recommendation systems have become essential for identifying relevant items based on user preferences. The domain of sequential recommendation aims to capture evolving user preferences over time. To address dynamic preference, various contrastive learning methods have been proposed to target data sparsity, a challenge in recommendation systems due to the limited user-item interactions. In this paper, we are the first to apply the Fisher-Merging method to Sequential Recommendation, addressing and resolving practical challenges associated with it. This approach ensures robust fine-tuning by merging the parameters of multiple models, resulting in improved overall performance. Through extensive experiments, we demonstrate the effectiveness of our proposed methods, highlighting their potential to advance the state-of-the-art in sequential learning and recommendation systems.

翻译：随着在线平台和服务的指数级增长，推荐系统已成为根据用户偏好识别相关项目的关键技术。序列推荐领域旨在捕捉用户随时间演变的偏好。为应对动态偏好问题，研究者提出了多种对比学习方法，以解决推荐系统中因用户-项目交互数据有限而导致的数据稀疏性挑战。本文首次将Fisher合并方法应用于序列推荐，解决并克服了该方法在实际应用中的相关难题。该策略通过合并多个模型的参数实现鲁棒微调，从而提升整体性能。通过大量实验，我们验证了所提方法的有效性，并展示了其在推动序列学习与推荐系统前沿发展方面的潜力。