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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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Opening the Black Box: Interpretable Remedies for Popularity Bias in Recommender Systems
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Real-Time RAG for the Identification of Supply Chain Vulnerabilities
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Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation
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Enhanced NIRMAL Optimizer With Damped Nesterov Acceleration: A Comparative Analysis
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A Node-Aware Dynamic Quantization Approach for Graph Collaborative Filtering
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Modeling User Preferences as Distributions for Optimal Transport-based Cross-domain Recommendation under Non-overlapping Settings
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EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation
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Cross-Modal Prototype Augmentation and Dual-Grained Prompt Learning for Social Media Popularity Prediction
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Spacetime-GR: A Spacetime-Aware Generative Model for Large Scale Online POI Recommendation
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Similarity-Based Supervised User Session Segmentation Method for Behavior Logs
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Reading Between the Lines: A Study of Thematic Bias in Book Recommender Systems
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On Evaluating the Adversarial Robustness of Foundation Models for Multimodal Entity Linking
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On the Effectiveness of Graph Reordering for Accelerating Approximate Nearest Neighbor Search on GPU
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Curriculum Approximate Unlearning for Session-based Recommendation
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Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns
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Global-Distribution Aware Scenario-Specific Variational Representation Learning Framework
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Diversity-Augmented Negative Sampling for Implicit Collaborative Filtering
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Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems
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RewardRank: Optimizing True Learning-to-Rank Utility
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Democratizing News Recommenders: Modeling Multiple Perspectives for News Candidate Generation with VQ-VAE
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InPars+: Supercharging Synthetic Data Generation for Information Retrieval Systems
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CARE: Contextual Adaptation of Recommenders for LLM-based Conversational Recommendation
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