·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Anthropic apologizes for invisible Claude Fable guardrails1h◆Google DeepMind is worried about what happens when millions of agents start to interact2h◆Deezer launches an AI music detector for other streaming services5h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing9h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning9h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!9h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions9h◆The Impossibility of Eliciting Latent Knowledge9h◆Mapping Scientific Literature with Large Language Models and Topic Modeling9h◆Grounding Computer Use Agents on Human Demonstrations9h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models9h◆LSTM based IoT Device Identification9h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse9h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM9h◆Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models9h◆DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?9h◆Higher order PCA-like rotation-invariant features for detailed shape descriptors modulo rotation9h◆CoVEBench: Can Video Editing Models Handle Complex Instructions?9h◆Minimal surfaces, Knots, and Neural Networks9h◆CCKS: Consensus-based Communication and Knowledge Sharing9h◆Anthropic apologizes for invisible Claude Fable guardrails1h◆Google DeepMind is worried about what happens when millions of agents start to interact2h◆Deezer launches an AI music detector for other streaming services5h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing9h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning9h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!9h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions9h◆The Impossibility of Eliciting Latent Knowledge9h◆Mapping Scientific Literature with Large Language Models and Topic Modeling9h◆Grounding Computer Use Agents on Human Demonstrations9h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models9h◆LSTM based IoT Device Identification9h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse9h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM9h◆Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models9h◆DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?9h◆Higher order PCA-like rotation-invariant features for detailed shape descriptors modulo rotation9h◆CoVEBench: Can Video Editing Models Handle Complex Instructions?9h◆Minimal surfaces, Knots, and Neural Networks9h◆CCKS: Consensus-based Communication and Knowledge Sharing9h◆
News/DiffCold: A Diffusion-based Generative Model for Cold-Start Item Recommendation
arxiv
PublishedJune 11, 2026 at 4:00 AM
—neutral

DiffCold: A Diffusion-based Generative Model for Cold-Start Item Recommendation

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.12245v1 Announce Type: cross Abstract: 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{seesa

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivMODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning9harxivPosition: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!9harxivGeneralizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions9harxivThe Impossibility of Eliciting Latent Knowledge9h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews