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.
Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning
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DREAM proposes intent-aware tokenization, frozen-model evaluation, and dynamic beams to refine early SID assignments and improve cold-start performance in generative recommenders on Amazon benchmarks.
This survey organizes generative recommendation into data, model, and task dimensions, identifying five advantages including world knowledge integration and creative generation while noting challenges in benchmarks and efficiency.
Introduces semantic Pareto-DQN for multi-objective recommendation that sustains trajectory variance to improve diversity and fairness on MovieLens with limited engagement loss.
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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DREAM: Dynamic Refinement of Early Assignment Mappings
DREAM proposes intent-aware tokenization, frozen-model evaluation, and dynamic beams to refine early SID assignments and improve cold-start performance in generative recommenders on Amazon benchmarks.
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A Survey on Generative Recommendation: Data, Model, and Tasks
This survey organizes generative recommendation into data, model, and task dimensions, identifying five advantages including world knowledge integration and creative generation while noting challenges in benchmarks and efficiency.
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Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
Introduces semantic Pareto-DQN for multi-objective recommendation that sustains trajectory variance to improve diversity and fairness on MovieLens with limited engagement loss.