Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation

A series of graph filtering (GF)-based collaborative filtering (CF) showcasesstate-of-the-art performance on the recommendation accuracy by using a low-passfilter (LPF) without a training process. However, conventional GF-based CFapproaches mostly perform matrix decomposition on the item-item similaritygraph to realize the ideal LPF, which results in a non-trivial computationalcost and thus makes them less practical in scenarios where rapidrecommendations are essential. In this paper, we propose Turbo-CF, a GF-basedCF method that is both training-free and matrix decomposition-free. Turbo-CFemploys a polynomial graph filter to circumvent the issue of expensive matrixdecompositions, enabling us to make full use of modern computer hardwarecomponents (i.e., GPU). Specifically, Turbo-CF first constructs an item-itemsimilarity graph whose edge weights are effectively regulated. Then, our ownpolynomial LPFs are designed to retain only low-frequency signals withoutexplicit matrix decompositions. We demonstrate that Turbo-CF is extremely fastyet accurate, achieving a runtime of less than 1 second on real-world benchmarkdatasets while achieving recommendation accuracies comparable to bestcompetitors.