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🏛️ The Information Retrieval Crypt
cs.IR: Where Information Retrieval papers rest without their code.
11622
Total Papers
9061
No Code
371
Twilight
2190
Has Code
18.8%
Survival Rate
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Looking for Fairness in Recommender Systems
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Context-Aware Search and Retrieval Over Erasure Channels
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Similarity-Guided Diffusion for Contrastive Sequential Recommendation
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FedFlex: Federated Learning for Diverse Netflix Recommendations
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Unraveling the Biomarker Prospects of High-Altitude Diseases: Insights from Biomolecular Event Network Constructed using Text Mining
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LLM-Driven Dual-Level Multi-Interest Modeling for Recommendation
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Extracting Document Relations from Search Corpus by Marginalizing over User Queries
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Riding the Carousel: The First Extensive Eye Tracking Analysis of Browsing Behavior in Carousel Recommenders
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SLIF-MR: Self-loop Iterative Fusion of Heterogeneous Auxiliary Information for Multimodal Recommendation
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MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora
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Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems
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Item-centric Exploration for Cold Start Problem
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Balancing Semantic Relevance and Engagement in Related Video Recommendations
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Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation
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Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging
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Document Similarity Enhanced IPS Estimation for Unbiased Learning to Rank
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Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study
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SEARA: An Automated Approach for Obtaining Optimal Retrievers
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USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations
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Unconditional Diffusion for Generative Sequential Recommendation
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Nyay-Darpan: Enhancing Decision Making Through Summarization and Case Retrieval for Consumer Law in India
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Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations
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