1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)
Moumita Bhattacharya, Vito Ostuni, Sudarshan Lamkhede
Search and recommendation systems are essential in many services, and they are often developed separately, leading to complex maintenance and technical debt. In this paper, we pres…
Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation
Mucun Tian, Chun Guo, Vito Ostuni +1
Learning-to-Rank (LTR) models trained from implicit feedback (e.g. clicks) suffer from inherent biases. A well-known one is the position bias -- documents in top positions are more…