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🏛️ The Information Retrieval Crypt
cs.IR: Where Information Retrieval papers rest without their code.
11581
Total Papers
9061
No Code
371
Twilight
2149
Has Code
18.6%
Survival Rate
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Exploring Human-Like Thinking in Search Simulations with Large Language Models
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Towards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation
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CHIME: A Compressive Framework for Holistic Interest Modeling
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User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems
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Investigating and Mitigating Stereotype-aware Unfairness in LLM-based Recommendations
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Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation
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Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising
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Beyond Unimodal Boundaries: Generative Recommendation with Multimodal Semantics
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MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation
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HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation
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Eternal Rest
Improvement Graph Convolution Collaborative Filtering with Weighted addition input
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RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning
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Enhanced Bloom's Educational Taxonomy for Fostering Information Literacy in the Era of Large Language Models
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Improving RAG for Personalization with Author Features and Contrastive Examples
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SUNAR: Semantic Uncertainty based Neighborhood Aware Retrieval for Complex QA
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Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions
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Towards Carbon Footprint-Aware Recommender Systems for Greener Item Recommendation
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Leveraging Retrieval Augmented Generative LLMs For Automated Metadata Description Generation to Enhance Data Catalogs
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LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance
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The Cartographer
Multi-Behavior Recommender Systems: A Survey
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Can LLMs Outshine Conventional Recommenders? A Comparative Evaluation
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HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation
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