  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.
