233 citations · 531 across the 34 of their papers we have counts for
16 papers · 1 filter
RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents
Imad Aouali, Flavian Vasile, Otmane Sakhi +2
LLM recommendation agents increasingly produce structured recommendation reports: sets of items accompanied by natural-language justifications. Yet existing evaluations often reduc…
AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting
Veronika Shilova, Ludovic Dos Santos, Flavian Vasile +2
In digital advertising, the selection of the optimal item (recommendation) and its best creative presentation (creative optimization) have traditionally been considered separate di…
Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation
Imad Aouali, Amine Benhalloum, Martin Bompaire +5
Both in academic and industry-based research, online evaluation methods are seen as the golden standard for interactive applications like recommendation systems. Naturally, the rea…
Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation
Imad Aouali, Achraf Ait Sidi Hammou, Otmane Sakhi +2
We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation. Our approach allows off-policy estimation of the reward in t…
From Clicks to Conversions: Recommendation for long-term reward
Philomène Chagniot, Flavian Vasile, David Rohde
Recommender systems are often optimised for short-term reward: a recommendation is considered successful if a reward (e.g. a click) can be observed immediately after the recommenda…
Learning from Bandit Feedback: An Overview of the State-of-the-art
Olivier Jeunen, Dmytro Mykhaylov, David Rohde +3
In machine learning we often try to optimise a decision rule that would have worked well over a historical dataset; this is the so called empirical risk minimisation principle. In…
Recommendation System-based Upper Confidence Bound for Online Advertising
Nhan Nguyen-Thanh, Dana Marinca, Kinda Khawam +5
In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for…
On the Value of Bandit Feedback for Offline Recommender System Evaluation
Olivier Jeunen, David Rohde, Flavian Vasile
In academic literature, recommender systems are often evaluated on the task of next-item prediction. The procedure aims to give an answer to the question: "Given the natural sequen…
Causal Embeddings for Recommendation: An Extended Abstract
Stephen Bonner, Flavian Vasile
Recommendations are commonly used to modify user's natural behavior, for example, increasing product sales or the time spent on a website. This results in a gap between the ultimat…
Three Methods for Training on Bandit Feedback
Dmytro Mykhaylov, David Rohde, Flavian Vasile +2
There are three quite distinct ways to train a machine learning model on recommender system logs. The first method is to model the reward prediction for each possible recommendatio…
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
David Rohde, Stephen Bonner, Travis Dunlop +2
Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to frien…
Siamese Cookie Embedding Networks for Cross-Device User Matching
Ugo Tanielian, Anne-Marie Tousch, Flavian Vasile
Over the last decade, the number of devices per person has increased substantially. This poses a challenge for cookie-based personalization applications, such as online search and…
Specializing Joint Representations for the task of Product Recommendation
Thomas Nedelec, Elena Smirnova, Flavian Vasile
We propose a unified product embedded representation that is optimized for the task of retrieval-based product recommendation. To this end, we introduce a new way to fuse modality-…
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Elena Smirnova, Flavian Vasile
Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session…
Causal Embeddings for Recommendation
Stephen Bonner, Flavian Vasile
Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website. This results in…
Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
Flavian Vasile, Elena Smirnova, Alexis Conneau
We propose Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata. Such scenarios are frequently encountered in applica…