44 citations · 189 across the 15 of their papers we have counts for
17 papers
Controllable User Simulation
Guy Tennenholtz, Ofer Meshi, Amir Globerson +3
Using offline datasets to evaluate conversational agents often fails to cover rare scenarios or to support testing new policies. This has motivated the use of controllable user sim…
A Unified Model and Document Representation for On-Device Retrieval-Augmented Generation
Julian Killingback, Ofer Meshi, Henry Li +2
Traditional Retrieval-Augmented Generation (RAG) approaches generally assume that retrieval and generation occur on powerful servers removed from the end user. While this reduces l…
Asking Clarifying Questions for Preference Elicitation With Large Language Models
Ali Montazeralghaem, Guy Tennenholtz, Craig Boutilier +1
Large Language Models (LLMs) have made it possible for recommendation systems to interact with users in open-ended conversational interfaces. In order to personalize LLM responses,…
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…
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
Haolun Wu, Ofer Meshi, Masrour Zoghi +4
Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, l…
Overcoming Prior Misspecification in Online Learning to Rank
Javad Azizi, Ofer Meshi, Masrour Zoghi +1
The recent literature on online learning to rank (LTR) has established the utility of prior knowledge to Bayesian ranking bandit algorithms. However, a major limitation of existing…