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09/08/2023
AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting
In digital advertising, the selection of the optimal item (recommendatio...
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09/18/2022
Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation
Both in academic and industry-based research, online evaluation methods ...
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08/10/2022
A Scalable Probabilistic Model for Reward Optimizing Slate Recommendation
We introduce Probabilistic Rank and Reward model (PRR), a scalable proba...
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06/28/2022
Welfare-Optimized Recommender Systems
We present a recommender system based on the Random Utility Model. Onlin...
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09/02/2021
What Users Want? WARHOL: A Generative Model for Recommendation
Current recommendation approaches help online merchants predict, for eac...
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07/26/2021
Combining Reward and Rank Signals for Slate Recommendation
We consider the problem of slate recommendation, where the recommender s...
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11/13/2020
Improving Offline Contextual Bandits with Distributional Robustness
This paper extends the Distributionally Robust Optimization (DRO) approa...
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09/01/2020
From Clicks to Conversions: Recommendation for long-term reward
Recommender systems are often optimised for short-term reward: a recomme...
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08/28/2020
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals
A common task for recommender systems is to build a pro le of the intere...
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10/02/2019
Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks
The combination of the re-parameterization trick with the use of variati...
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09/18/2019
Learning from Bandit Feedback: An Overview of the State-of-the-art
In machine learning we often try to optimise a decision rule that would ...
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09/17/2019
Relaxed Softmax for learning from Positive and Unlabeled data
In recent years, the softmax model and its fast approximations have beco...
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09/09/2019
Recommendation System-based Upper Confidence Bound for Online Advertising
In this paper, the method UCB-RS, which resorts to recommendation system...
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07/26/2019
On the Value of Bandit Feedback for Offline Recommender System Evaluation
In academic literature, recommender systems are often evaluated on the t...
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06/14/2019
Distributionally Robust Counterfactual Risk Minimization
This manuscript introduces the idea of using Distributionally Robust Opt...
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04/24/2019
Three Methods for Training on Bandit Feedback
There are three quite distinct ways to train a machine learning model on...
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04/10/2019
Causal Embeddings for Recommendation: An Extended Abstract
Recommendations are commonly used to modify user's natural behavior, for...
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08/02/2018
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
Recommender Systems are becoming ubiquitous in many settings and take ma...
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05/22/2018
Adversarial Training of Word2Vec for Basket Completion
In recent years, the Word2Vec model trained with the Negative Sampling l...
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05/22/2018
Neural Generative Models for Global Optimization with Gradients
The aim of global optimization is to find the global optimum of arbitrar...
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03/28/2018
Siamese Cookie Embedding Networks for Cross-Device User Matching
Over the last decade, the number of devices per person has increased sub...
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01/22/2018
Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces
Learning to optimize - the idea that we can learn from data algorithms t...
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07/25/2016
