Abstract: Current recommendation algorithms mainly focus on using deep learning techniques to enhance recommendation accuracy,thereby providing users with a collection of content they are interested in.However,the recommendation results obtained by such methods often have high computational costs and model redundancy,making them unsuitable for resource-constrained scenarios.To address these issues,a collaborative optimization framework(DySparseHKD) that integrates dynamic sparsity and he-terogeneous knowledge distillation is proposed.This framework builds a lightweight recommendation model that reduces the number of parameters while retaining key features.A dynamic sparsity rate allocation method based on interaction redundancy is proposed to capture more efficient parameter configurations.The training trajectory of the teacher model is utilized to achieve progressive knowledge transfer,alleviating the knowledge gap between heterogeneous models.The knowledge transfer granularity is dynamically adjusted according to the current learning state of the student model to improve transfer efficiency.Finally,the deep decoupling of model complexity and recommendation performance is achieved through joint optimization objectives.Experiments on three real datasets show that the proposed model achieves an organic integration of model efficiency and recommendation effect while maintaining lower complexity.

Key words: Dynamic sparse technology, Heterogeneous knowledge distillation, Knowledge transfer, Collaborative filtering, Lightweight recommendation model