[2404.04265] Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation

Computer Science > Information Retrieval arXiv:2404.04265 (cs) [Submitted on 18 Mar 2024 (v1), last revised 25 Mar 2026 (this version, v2)] Title:Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation Authors:Yining Wu, Shengyu Duan, Gaole Sai, Chenhong Cao, Guobing Zou View a PDF of the paper titled Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation, by Yining Wu and 3 other authors View PDF Abstract:Matrix factorization (MF) is a widely used collaborative filtering (CF) algorithm for recommendation systems (RSs), due to its high prediction accuracy, great flexibility and high efficiency in big data processing. However, with the dramatically increased number of users/items in current RSs, the computational complexity for training a MF model largely increases. Many existing works have accelerated MF, by either putting in additional computational resources or utilizing parallel systems, introducing a large cost. In this paper, we propose algorithmic methods to accelerate MF, without inducing any additional computational resources. In specific, we observe fine-grained structured sparsity in the decomposed feature matrices when considering a certain threshold. The fine-grained structured sparsity causes a large amount of unnecessary operations during both matrix multiplication and latent factor update, increasing the computational time of the MF training process. Based on the observation, we firstly propose to rearrange the feat...