ANCHOR creates synthetic noise labels via recommender-in-the-loop LLM agents and trains a parametric recognizer on them to perform supervised denoising of implicit feedback.
Ruleagent: Discovering rules for recommendation denoising with autonomous language agents
4 Pith papers cite this work. Polarity classification is still indexing.
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A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
CoARS enables co-evolving recommender and user agents by using interaction-derived rewards and self-distilled credit assignment to internalize multi-turn feedback into model parameters, outperforming prior agentic baselines.
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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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.