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08/19/2023
Large Language Models as Zero-Shot Conversational Recommenders
In this paper, we present empirical studies on conversational recommenda...
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12/22/2022
Local Policy Improvement for Recommender Systems
Recommender systems aim to answer the following question: given the item...
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06/14/2019
Learning Correlated Latent Representations with Adaptive Priors
Variational Auto-Encoders (VAEs) have been widely applied for learning c...
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05/14/2019
Correlated Variational Auto-Encoders
Variational Auto-Encoders (VAEs) are capable of learning latent represen...
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08/20/2018
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
The goal of a recommender system is to show its users items that they wi...
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02/16/2018
Variational Autoencoders for Collaborative Filtering
We extend variational autoencoders (VAEs) to collaborative filtering for...
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10/17/2017
On the challenges of learning with inference networks on sparse, high-dimensional data
We study parameter estimation in Nonlinear Factor Analysis (NFA) where t...
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10/31/2016
Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical in...
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10/23/2015
Modeling User Exposure in Recommendation
Collaborative filtering analyzes user preferences for items (e.g., books...
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11/07/2014
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference
Beta process is the standard nonparametric Bayesian prior for latent fac...
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12/20/2013
