Recommendation


Recommendation is the task of providing personalized suggestions to users based on their preferences and behavior.

PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation

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Jun 05, 2025
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Population-Proportional Preference Learning from Human Feedback: An Axiomatic Approach

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Jun 05, 2025
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Heterogeneous Sequel-Aware Graph Neural Networks for Sequential Learning

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Jun 05, 2025
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Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation

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Jun 05, 2025
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GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems

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Jun 04, 2025
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Stronger Baselines for Retrieval-Augmented Generation with Long-Context Language Models

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Jun 04, 2025
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Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid

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Jun 04, 2025
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Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems

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Jun 04, 2025
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N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion

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Jun 04, 2025
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MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP

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Jun 04, 2025
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