activity
20102024
most citedImpatient Bandits: Optimizing Recommendations for the Long-Term Without Delay

14 citations · 18 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Long-term Off-Policy Evaluation and Learning

Yuta Saito, Himan Abdollahpouri, Jesse Anderton +2

Short- and long-term outcomes of an algorithm often differ, with damaging downstream effects. A known example is a click-bait algorithm, which may increase short-term clicks but da…

cs.IR2024

Towards Graph Foundation Models for Personalization

Andreas Damianou, Francesco Fabbri, Paul Gigioli +4

In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build st…

cs.IR2024

Generalized User Representations for Transfer Learning

Ghazal Fazelnia, Sanket Gupta, Claire Keum +3

We present a novel framework for user representation in large-scale recommender systems, aiming at effectively representing diverse user taste in a generalized manner. Our approach…

cs.LG202314 cited

Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay

Thomas M. McDonald, Lucas Maystre, Mounia Lalmas +2

Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study…

cs.IR20104 cited

Exploring a Multidimensional Representation of Documents and Queries (extended version)

Benjamin Piwowarski, Ingo Frommholz, Mounia Lalmas +1

In Information Retrieval (IR), whether implicitly or explicitly, queries and documents are often represented as vectors. However, it may be more beneficial to consider documents an…