  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.
