5 citations · 5 across the 5 of their papers we have counts for
5 papers
Efficient Item ID Generation for Large-Scale LLM-based Recommendation
Anushya Subbiah, Vikram Aggarwal, James Pine +3
Integrating product catalogs and user behavior into LLMs can enhance recommendations with broad world knowledge, but the scale of real-world item catalogs, often containing million…
REGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives
Kun Su, Krishna Sayana, Hubert Pham +8
This paper introduces a novel dataset REGEN (Reviews Enhanced with GEnerative Narratives), designed to benchmark the conversational capabilities of recommender Large Language Model…
Beyond Retrieval: Generating Narratives in Conversational Recommender Systems
Krishna Sayana, Raghavendra Vasudeva, Yuri Vasilevski +6
The recent advances in Large Language Model's generation and reasoning capabilities present an opportunity to develop truly conversational recommendation systems. However, effectiv…
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies
Chih-Wei Hsu, Martin Mladenov, Ofer Meshi +8
Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold s…
FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement
Liam Hebert, Marialena Kyriakidi, Hubert Pham +4
Recent proposals in recommender systems represent items with their textual description, using a large language model. They show better results on standard benchmarks compared to an…