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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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MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
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Research Knowledge Graphs: the Shifting Paradigm of Scholarly Information Representation
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Rethinking Contrastive Learning in Session-based Recommendation
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Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion
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AI4Contracts: LLM & RAG-Powered Encoding of Financial Derivative Contracts
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Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity
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CoRank: LLM-Based Compact Reranking with Document Features for Scientific Retrieval
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The Effects of Demographic Instructions on LLM Personas
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Boosting Text-to-Chart Retrieval through Training with Synthesized Semantic Insights
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Death by README
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward
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Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems
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Uncertainty in Repeated Implicit Feedback as a Measure of Reliability
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The Cartographer
Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective
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Memory Assisted LLM for Personalized Recommendation System
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Effective Inference-Free Retrieval for Learned Sparse Representations
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Eternal Rest
Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection
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Leveraging Decoder Architectures for Learned Sparse Retrieval
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Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics
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SemCORE: A Semantic-Enhanced Generative Cross-Modal Retrieval Framework with MLLMs
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CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation
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Slow Thinking for Sequential Recommendation
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Breaking the Lens of the Telescope: Online Relevance Estimation over Large Retrieval Sets
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