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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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Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems
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Style4Rec: Enhancing Transformer-based E-commerce Recommendation Systems with Style and Shopping Cart Information
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Graph Contrastive Learning on Multi-label Classification for Recommendations
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Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations
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Towards Reliable Testing for Multiple Information Retrieval System Comparisons
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Multi-label Cross-lingual automatic music genre classification from lyrics with Sentence BERT
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Political Events using RAG with LLMs
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Citation Structural Diversity: A Novel and Concise Metric Combining Structure and Semantics for Literature Evaluation
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GenTREC: The First Test Collection Generated by Large Language Models for Evaluating Information Retrieval Systems
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Who Gets Recommended? Investigating Gender, Race, and Country Disparities in Paper Recommendations from Large Language Models
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Hgformer: Hyperbolic Graph Transformer for Recommendation
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Don't Lose Yourself: Boosting Multimodal Recommendation via Reducing Node-neighbor Discrepancy in Graph Convolutional Network
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BRIDGE: Bundle Recommendation via Instruction-Driven Generation
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CiteBART: Learning to Generate Citations for Local Citation Recommendation
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Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark
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Enhancing Supply Chain Transparency in Emerging Economies Using Online Contents and LLMs
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Level-Navi Agent: A Framework and benchmark for Chinese Web Search Agents
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An Agent Framework for Real-Time Financial Information Searching with Large Language Models
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CRS Arena: Crowdsourced Benchmarking of Conversational Recommender Systems
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SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems
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Combining knowledge graphs and LLMs for hazardous chemical information management and reuse
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Bridging Conversational and Collaborative Signals for Conversational Recommendation
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