6 papers
Mult-DPO: Multinomial Direct Preference Optimization for Recommender Systems
Yaochen Zhu, Harald Steck, James McInerney +4
Direct preference optimization (DPO) is a simple and effective alignment strategy for large language models (LLMs) based on pairwise preferences. In recommender systems, however, u…
Beyond Continuity: Challenges of Context Switching in Multi-Turn Dialogue with LLMs
Aditya Sinha, Harald Steck, Vito Ostuni +1
Users interacting with Large Language Models (LLMs) in a multi-turn conversation routinely refine their requests or pivot to new topics. LLMs, however, often miss these topic shift…
Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning
Yaochen Zhu, Harald Steck, Dawen Liang +4
Large language models (LLMs) are reshaping the recommender system paradigm by enabling users to express preferences and receive recommendations through conversations. Yet, aligning…
Does Weighting Improve Matrix Factorization for Recommender Systems?
Alex Ayoub, Samuel Robertson, Dawen Liang +2
Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic…
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System
Rohan Surana, Junda Wu, Zhouhang Xie +5
Conversational recommender systems (CRS) typically require extensive domain-specific conversational datasets, yet high costs, privacy concerns, and data-collection challenges sever…
Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems
Yaochen Zhu, Chao Wan, Harald Steck +4
Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users. While large language models (LLMs) enhance CRS with their…