In digital advertising, the selection of the optimal item (recommendatio...
Both in academic and industry-based research, online evaluation methods ...
We introduce Probabilistic Rank and Reward model (PRR), a scalable
proba...
We present a recommender system based on the Random Utility Model. Onlin...
Current recommendation approaches help online merchants predict, for eac...
We consider the problem of slate recommendation, where the recommender s...
This paper extends the Distributionally Robust Optimization (DRO) approa...
Recommender systems are often optimised for short-term reward: a
recomme...
A common task for recommender systems is to build a pro le of the intere...
The combination of the re-parameterization trick with the use of variati...
In machine learning we often try to optimise a decision rule that would ...
In recent years, the softmax model and its fast approximations have beco...
In this paper, the method UCB-RS, which resorts to recommendation system...
In academic literature, recommender systems are often evaluated on the t...
This manuscript introduces the idea of using Distributionally Robust
Opt...
There are three quite distinct ways to train a machine learning model on...
Recommendations are commonly used to modify user's natural behavior, for...
Recommender Systems are becoming ubiquitous in many settings and take ma...
In recent years, the Word2Vec model trained with the Negative Sampling l...
The aim of global optimization is to find the global optimum of arbitrar...
Over the last decade, the number of devices per person has increased
sub...
Learning to optimize - the idea that we can learn from data algorithms t...
We propose Meta-Prod2vec, a novel method to compute item similarities fo...