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Found 14 papers, 3 papers with code
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies
In this work, we describe a simulation methodology used to augment (and reduce) the use of live experiments.
Embedding-Aligned Language Models
Our embedding-aligned guided language (EAGLE) agent is trained to iteratively steer the LLM's generation towards optimal regions of the latent embedding space, w. r. t.
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates.
Preference Elicitation with Soft Attributes in Interactive Recommendation
Leveraging concept activation vectors for soft attribute semantics, we develop novel preference elicitation methods that can accommodate soft attributes and bring together both item and attribute-based preference elicitation.
Factual and Personalized Recommendations using Language Models and Reinforcement Learning
Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences.
Demystifying Embedding Spaces using Large Language Models
Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format.
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e. g., clicks, item consumption, ratings).
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e. g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention in recent years.
Differentiable Meta-Learning of Bandit Policies
Exploration policies in Bayesian bandits maximize the average reward over problem instances drawn from some distribution P. In this work, we learn such policies for an unknown distribution P using samples from P. Our approach is a form of meta-learning and exploits properties of P without making strong assumptions about its form.
Meta-Learning Bandit Policies by Gradient Ascent
Most bandit policies are designed to either minimize regret in any problem instance, making very few assumptions about the underlying environment, or in a Bayesian sense, assuming a prior distribution over environment parameters.
Differentiable Bandit Exploration
In this work, we learn such policies for an unknown distribution $\mathcal{P}$ using samples from $\mathcal{P}$.
RecSim: A Configurable Simulation Platform for Recommender Systems
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users.
Empirical Bayes Regret Minimization
In this work, we pioneer the idea of algorithm design by minimizing the empirical Bayes regret, the average regret over problem instances sampled from a known distribution.
