  In this age where data is abundant, the ability to distill meaningful
insights from the sea of information is essential. Our research addresses the
computational and resource inefficiencies that current Sequential Recommender
Systems (SRSs) suffer from. especially those employing attention-based models
like SASRec, These systems are designed for next-item recommendations in
various applications, from e-commerce to social networks. However, such systems
suffer from substantial computational costs and resource consumption during the
inference stage. To tackle these issues, our research proposes a novel method
that combines automatic pruning techniques with advanced model architectures.
We also explore the potential of resource-constrained Neural Architecture
Search (NAS), a technique prevalent in the realm of recommendation systems, to
fine-tune models for reduced FLOPs, latency, and energy usage while retaining
or even enhancing accuracy. The main contribution of our work is developing the
Elastic Architecture Search for Efficient Long-term Sequential Recommender
Systems (EASRec). This approach aims to find optimal compact architectures for
attention-based SRSs, ensuring accuracy retention. EASRec introduces data-aware
gates that leverage historical information from input data batch to improve the
performance of the recommendation network. Additionally, it utilizes a dynamic
resource constraint approach, which standardizes the search process and results
in more appropriate architectures. The effectiveness of our methodology is
validated through exhaustive experiments on three benchmark datasets, which
demonstrates EASRec's superiority in SRSs. Our research set a new standard for
future exploration into efficient and accurate recommender systems, signifying
a substantial advancement within this swiftly advancing field.
