From Next-Item to Multi-User Generation: Reformulating Cold-Start Recommendation via Parallel Generative Discrete Diffusion

Most existing recommender systems are built from a user-centric perspective, aiming to recommend the most suitable content to each user. However, this paradigm often fails in cold-start scenarios, where the paucity of interaction feedback compromises both reliable representation learning and sufficient initial exposure within massive candidate pools. This paper advocates a paradigm shift from ”Next-Item” to ”Multi-User Generation” after revisiting the underlying logic of cold-start recommendation, and reformulates the task as **Item-centric Multi-User Generation**, aiming to proactively identify seed users to break the passive exposure deadlock. However, this transition is challenging, as the multi-modal target user distributions and the non-sequential nature of interactions pose a severe inductive bias mismatch for traditional user-centric methods. To address this challenge, we propose **P**arallel Generative **D**iscrete **D**iffusion (${\text{PD}}^2$), which employs a permutation-invariant Masked Discrete Diffusion mechanism to construct a direct mapping from item content to the latent user space, while eschewing autoregressive constraints. We also develop Interval-Aware Spatio-Temporal Encoding, incorporating temporal biasing with positional encoding to capture the temporal evolution of group interests and ensure multi-user extrapolation. With this design, ${\text{PD}}^2$ empowers effective *Multi-Group Look-ahead Prediction* to explore diverse interest groups and *Scalable Parallel Retrieval* to ensure high-efficiency decoding. Extensive experiments on two public real-world datasets and large-scale online A/B tests demonstrate that ${\text{PD}}^2$ outperforms existing methods, especially in cold-start scenarios.