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

arXiv:2404.14243v1 Announce Type: cross
Abstract: 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 …

abstract accuracy art arxiv collaborative collaborative filtering cs.ai cs.ir cs.it cs.lg cs.si filter filtering free graph however low math.it matrix performance process recommendation results series state training turbo type