50 citations · 108 across the 18 of their papers we have counts for
5 papers · 1 filter
Demystifying Embedding Spaces using Large Language Models
Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu +6
Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format. Nevertheless, t…
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Craig Boutilier, Martin Mladenov, Guy Tennenholtz
Modern recommender systems lie at the heart of complex ecosystems that couple the behavior of users, content providers, advertisers, and other actors. Despite this, the focus of th…
Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems
Siddharth Prasad, Martin Mladenov, Craig Boutilier
Users derive value from a recommender system (RS) only to the extent that it is able to surface content (or items) that meet their needs/preferences. While RSs often have a compreh…
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics
Guy Tennenholtz, Martin Mladenov, Nadav Merlis +2
While popularity bias is recognized to play a crucial role in recommmender (and other ranking-based) systems, detailed analysis of its impact on collective user welfare has largely…
Reinforcement Learning with History-Dependent Dynamic Contexts
Guy Tennenholtz, Nadav Merlis, Lior Shani +2
We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a novel reinforcement learning framework for history-dependent environments that generalizes the contextual MDP…