5 citations · 5 across the 5 of their papers we have counts for
5 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…
Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion
Pengcheng Jiang, Judith Yue Li, Moonkyung Ryu +8
Many modern retrieval problems are set-valued: given a broad intent, the system must return a collection of results that optimizes higher-order properties (e.g., diversity, coverag…
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…