Escaping the Euclidean Void: Manifold-Informed Flow Matching for Sequential Recommendation
DGX agentarXiv:2607.23762v1 Announce Type: cross Abstract: Conventional recommenders capture users' preferences by optimizing observed user-item relations, whereas continuous generative recommendation addition