DiffCold: A Diffusion-based Generative Model for Cold-Start Item Recommendation
Cold-start item recommendation remains a persistent challenge in real-world systems due to the absence of interaction histories. While prior models attempt to bridge this gap using item content features, they universally suffer from the \textbf{seesaw dilemma}: enhancing performance for cold items inevitably degrades performance for warm items, and vice versa. We identify that this dilemma stems from a fundamental \textbf{distributional disparity}: warm item embeddings occupy a complex ``behavioral manifold" shaped by rich interaction signals, whereas cold item embeddings are constrained to a ``semantic manifold" derived solely from auxiliary content. Existing methods often force a rigid ...
| Authors | Kangning Zhang, Yingjie Qin, Weinan Zhang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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