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Found 5 papers, 0 papers with code
REGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives
Further, we establish an end-to-end modeling benchmark for the task of conversational recommendation, where models are trained to generate both recommendations and corresponding narratives conditioned on user history (items and critiques).
Beyond Retrieval: Generating Narratives in Conversational Recommender Systems
And to the best of our knowledge, represents the first attempt to analyze the capabilities of LLMs in understanding recommender signals and generating rich narratives.
FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement
With revised baselines for item ID-only models, this paper also establishes new competitive results for architectures that combine IDs and textual descriptions.
PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting
Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preferences.
User Embedding Model for Personalized Language Prompting
Modeling long histories plays a pivotal role in enhancing recommendation systems, allowing to capture user's evolving preferences, resulting in more precise and personalized recommendations.
