PaperFlow proposes a Profiling-Recommending-Adapting framework for longitudinal scientific paper recommendation and evaluates it on a new user-day benchmark with 24 simulated users, outperforming five baselines in ranking, behavioral alignment, and blind human evaluation.
Embracing Plasticity: Balancing Stability and Plasticity in Continual Recommender Systems
3 Pith papers cite this work. Polarity classification is still indexing.
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Adaptive Re-Ranking trains a classifier to route queries to BM25, MiniLM-L6-v2, or BGE-v2-m3 based on a utility label, yielding 1.15-53x lower median latency and competitive nDCG@10 versus always using the heaviest model.
A multi-turn RAG system combines learned sparse retrieval with LLM-conditioned rewriting, listwise reranking, and generation to handle conversational QA and unanswerable queries across four domains.
citing papers explorer
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PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams
PaperFlow proposes a Profiling-Recommending-Adapting framework for longitudinal scientific paper recommendation and evaluates it on a new user-day benchmark with 24 simulated users, outperforming five baselines in ranking, behavioral alignment, and blind human evaluation.
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Adaptive Re-Ranking
Adaptive Re-Ranking trains a classifier to route queries to BM25, MiniLM-L6-v2, or BGE-v2-m3 based on a utility label, yielding 1.15-53x lower median latency and competitive nDCG@10 versus always using the heaviest model.
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uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking
A multi-turn RAG system combines learned sparse retrieval with LLM-conditioned rewriting, listwise reranking, and generation to handle conversational QA and unanswerable queries across four domains.