Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation

arXiv:2404.04265v1 Announce Type: cross
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, …

abstract accuracy algorithm arxiv big big data big data processing collaborative collaborative filtering complexity computational cs.ir cs.lg current data data processing dynamic efficiency factorization filtering flexibility however matrix prediction processing pruning recommendation recommendation systems rss systems training type