19 citations · 23 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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…
Latent Variable Session-Based Recommendation
David Rohde, Stephen Bonner
Session based recommendation provides an attractive alternative to the traditional feature engineering approach to recommendation. Feature engineering approaches require hand tuned…