A series of graph filtering (GF)-based collaborative filtering (CF) showcases
state-of-the-art performance on the recommendation accuracy by using a low-pass
filter (LPF) without a training process. However, conventional GF-based CF
approaches mostly perform matrix decomposition on the item-item similarity
graph to realize the ideal LPF, which results in a non-trivial computational
cost and thus makes them less practical in scenarios where rapid
recommendations are essential. In this paper, we propose Turbo-CF, a GF-based
CF method that is both training-free and matrix decomposition-free. Turbo-CF
employs a polynomial graph filter to circumvent the issue of expensive matrix
decompositions, enabling us to make full use of modern computer hardware
components (i.e., GPU). Specifically, Turbo-CF first constructs an item-item
similarity graph whose edge weights are effectively regulated. Then, our own
polynomial LPFs are designed to retain only low-frequency signals without
explicit matrix decompositions. We demonstrate that Turbo-CF is extremely fast
yet accurate, achieving a runtime of less than 1 second on real-world benchmark
datasets while achieving recommendation accuracies comparable to best
competitors.

Turbo-CF 是一种基于图过滤和协同过滤的方法，不需要训练和矩阵分解，通过使用多项式图过滤器在不显式矩阵分解的情况下实现高效的推荐准确性，适用于快速推荐场景。