This study introduces an innovative approach aimed at the efficient pruning of neural networks, with a particular focus on their deployment on edge devices. Our method involves the integration of the Lottery Ticket Hypothesis (LTH) with the Knowledge Distillation (KD) framework, resulting in the formulation of three distinct pruning models. These models have been developed to address scalability issue in recommender systems, whereby the complexities of deep learning models have hindered their practical deployment. With judicious application of the pruning techniques, we effectively curtail the power consumption and model dimensions without compromising on accuracy. Empirical evaluation has been performed using two real world datasets from diverse domains against two baselines. Gratifyingly, our approaches yielded a GPU computation-power reduction of up to 66.67%. Notably, our study contributes to the field of recommendation system by pioneering the application of LTH and KD.

翻译：本研究提出了一种面向神经网络高效剪枝的创新方法，特别聚焦于其在边缘设备上的部署应用。通过融合彩票假说（LTH）与知识蒸馏（KD）框架，我们构建了三种差异化的剪枝模型。这些模型旨在解决推荐系统中的可扩展性瓶颈——深度学习模型的复杂性制约了实际部署。通过审慎运用剪枝技术，我们在不牺牲精度的前提下有效降低了功耗与模型规模。基于两个不同领域的真实世界数据集，我们以两项基线方法为基准开展实证评估。令人满意的是，本方法实现了高达66.67%的GPU算力缩减。值得关注的是，本研究通过开创性地将LTH与KD应用于推荐系统领域，为该领域做出了重要贡献。