In the era of data proliferation, efficiently sifting through vast information to extract meaningful insights has become increasingly crucial. This paper addresses the computational overhead and resource inefficiency prevalent in existing Sequential Recommender Systems (SRSs). We introduce an innovative approach combining pruning methods with advanced model designs. Furthermore, we delve into resource-constrained Neural Architecture Search (NAS), an emerging technique in recommender systems, to optimize models in terms of FLOPs, latency, and energy consumption while maintaining or enhancing accuracy. Our principal contribution is the development of a Data-aware Neural Architecture Search for Recommender System (DNS-Rec). DNS-Rec is specifically designed to tailor compact network architectures for attention-based SRS models, thereby ensuring accuracy retention. It incorporates data-aware gates to enhance the performance of the recommendation network by learning information from historical user-item interactions. Moreover, DNS-Rec employs a dynamic resource constraint strategy, stabilizing the search process and yielding more suitable architectural solutions. We demonstrate the effectiveness of our approach through rigorous experiments conducted on three benchmark datasets, which highlight the superiority of DNS-Rec in SRSs. Our findings set a new standard for future research in efficient and accurate recommendation systems, marking a significant step forward in this rapidly evolving field.

翻译：在数据激增的时代，高效筛选海量信息以提取有意义的洞察变得日益关键。本文针对现有序列推荐系统中普遍存在的计算开销与资源效率低下问题，提出了一种融合剪枝方法与先进模型设计的创新方案。进一步地，我们深入探讨了资源受限的神经架构搜索这一推荐系统新兴技术，旨在保持或提升模型精度的同时，优化其浮点运算量、延迟及能耗。我们的核心贡献是开发了一种面向推荐系统的数据感知神经架构搜索方法。该方法专为基于注意力机制的序列推荐模型定制紧凑的网络架构，从而确保精度得以保留。它通过引入数据感知门控机制，从历史用户-项目交互中学习信息以提升推荐网络的性能。此外，DNS-Rec采用动态资源约束策略，稳定搜索过程并生成更适配的架构解决方案。我们在三个基准数据集上进行了严谨的实验，结果验证了所提方法的有效性，并凸显了DNS-Rec在序列推荐系统中的优越性。本研究为高效精准的推荐系统未来研究设立了新标准，标志着这一快速发展领域的重要进展。