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Found 4 papers, 3 papers with code
A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems
Through experiments and case studies in two conversational recommendation scenarios, we show that our framework can adapt to a variety of conversational recommendation settings and effectively simulate users' personalized preferences.
How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation
Through multiple experiments on two widely-used datasets in the field of conversational recommendation, we highlight several issues with the current evaluation methods for user simulators based on LLMs: (1) Data leakage, which occurs in conversational history and the user simulator's replies, results in inflated evaluation results.
Knowledge Graph-enhanced Sampling for Conversational Recommender System
Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem.
Multi-Dimension Fusion Network for Light Field Spatial Super-Resolution using Dynamic Filters
Light field cameras have been proved to be powerful tools for 3D reconstruction and virtual reality applications.
