GSPRec: On Improving Item Representations in Graph Signal Processing for Collaborative Filtering

תקציר מקורי באנגליתarXiv:2505.11552v3 Announce Type: replace-cross Abstract: Graph-based collaborative filtering methods act as low-pass filters in the spectral domain and discard the intermediate-frequency components where community-level user preferences reside. Existing GSP-based methods address the loss through sophisticated filter designs, yet derive item representations from the user-item interaction matrix alone. The interaction matrix captures which items each user interacted with, but not which items appear close together in users' interaction sequences. We propose GSPRec, a graph spectral collaborative filtering framework that produces richer item spectral representations by incorporating item-item proximity derived from user interaction ordering before spectral filtering. GSPRec derives item-item