content.raw: Qilin 🔥a large scale multimodal dataset for search, recommendation and RAG research, released by Xiaohongshu & Tsinghua University
content.type: text
content.value: Qilin 🔥a large scale multimodal dataset for search, recommendation and RAG research, released by Xiaohongshu & Tsinghua University
content.type: new_line
content.type: new_line
content.raw: Dataset: 
content.type: text
content.value: Dataset: 
content.raw: https://huggingface.co/datasets/THUIR/Qilin
content.id: THUIR/Qilin
content.type: dataset
content.type: resource
content.url: https://huggingface.co/datasets/THUIR/Qilin
content.type: new_line
content.raw: Paper: 
content.type: text
content.value: Paper: 
content.label: Qilin: A Multimodal Information Retrieval Dataset with APP-level User
    Sessions (2503.00501)
content.raw: https://huggingface.co/papers/2503.00501
content.id: 2503.00501
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2503.00501
content.type: new_line
content.type: new_line
content.raw: ✨Multiple content modalities (text, images, video thumbnails)
content.type: text
content.value: ✨Multiple content modalities (text, images, video thumbnails)
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content.raw: ✨Rich user interaction data ( from Xiaohongshu’s 300M+ MAUs, 70%+ search penetration)
content.type: text
content.value: ✨Rich user interaction data ( from Xiaohongshu’s 300M+ MAUs, 70%+ search penetration)
content.type: new_line
content.raw: ✨Comprehensive evaluation metrics
content.type: text
content.value: ✨Comprehensive evaluation metrics
content.type: new_line
content.raw: ✨Support for RAG system development
content.type: text
content.value: ✨Support for RAG system development
rawContent: Qilin 🔥a large scale multimodal dataset for search, recommendation and RAG research, released by Xiaohongshu & Tsinghua University
  
  Dataset: https://huggingface.co/datasets/THUIR/Qilin
  Paper: https://huggingface.co/papers/2503.00501
  
  ✨Multiple content modalities (text, images, video thumbnails)
  ✨Rich user interaction data ( from Xiaohongshu’s 300M+ MAUs, 70%+ search penetration)
  ✨Comprehensive evaluation metrics
  ✨Support for RAG system development
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