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Information Science

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Information Science

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Research Frontiers in Information Retrieval Relevance Ranking

Develops algorithms and models for ranking search results based on contextual relevance and user information needs.

Neural Ranking Beyond Vector Similarity
Temporal Decay and Freshness in Relevance Signals
Cross-Modal Relevance in Heterogeneous Information Networks
Explainable Ranking: Bridging Black-Box Predictions and User Trust
Context-Aware Relevance in Conversational Search Systems
Fairness and Diversity Trade-offs in Ranking Algorithms
Semantic Drift Detection in Evolving Query Intents
Multi-Stakeholder Relevance: Beyond Single-User Optimization
Zero-Shot Ranking Across Unseen Domains
Implicit User Feedback as Relevance Ground Truth

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