4 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…
Item-Language Model for Conversational Recommendation
Li Yang, Anushya Subbiah, Hardik Patel +5
Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abili…
Improved Estimation of Ranks for Learning Item Recommenders with Negative Sampling
Anushya Subbiah, Steffen Rendle, Vikram Aggarwal
In recommendation systems, there has been a growth in the number of recommendable items (# of movies, music, products). When the set of recommendable items is large, training and e…