95 citations · 167 across the 6 of their papers we have counts for
6 papers
Affective Music Recommendation: A Rollout-Based World Model for Offline Preference Optimization
Audrey Chan, Aaron Labbé, Jacob Lavoie +4
Functional music applications, from consumer focus and sleep aids to clinical interventions, share a distinctive recommendation problem: success is defined by the listener's affect…
Contextual Preference Distribution Learning
Benjamin Hudson, Laurent Charlin, Emma Frejinger
Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we propose a sequential learning-and-optimi…
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
Yixin Wang, Dawen Liang, Laurent Charlin +1
The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question--…
Modeling User Exposure in Recommendation
Dawen Liang, Laurent Charlin, James McInerney +1
Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feed…
Dynamic Poisson Factorization
Laurent Charlin, Rajesh Ranganath, James McInerney +1
Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically…
Deep Exponential Families
Rajesh Ranganath, Linpeng Tang, Laurent Charlin +1
We describe \textit{deep exponential families} (DEFs), a class of latent variable models that are inspired by the hidden structures used in deep neural networks. DEFs capture a hie…