Bandits Warm-up Cold Recommender Systems

We address the cold start problem in recommendation systems assuming no contextual information is available neither about users, nor items. We consider the case in which we only have access to a set of ratings of items by users. Most of the existing works consider a batch setting, and use cross-validation to tune parameters. The classical method consists in minimizing the root mean square error over a training subset of the ratings which provides a factorization of the matrix of ratings, interpreted as a latent representation of items and users. Our contribution in this paper is 5-fold. First, we explicit the issues raised by this kind of batch setting for users or items with very few ratings. Then, we propose an online setting closer to the actual use of recommender systems; this setting is inspired by the bandit framework. The proposed methodology can be used to turn any recommender system dataset (such as Netflix, MovieLens,...) into a sequential dataset. Then, we explicit a strong and insightful link between contextual bandit algorithms and matrix factorization; this leads us to a new algorithm that tackles the exploration/exploitation dilemma associated to the cold start problem in a strikingly new perspective. Finally, experimental evidence confirm that our algorithm is effective in dealing with the cold start problem on publicly available datasets. Overall, the goal of this paper is to bridge the gap between recommender systems based on matrix factorizations and those based on contextual bandits

Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques

In many recommendation applications such as news recommendation, the items that can be rec- ommended come and go at a very fast pace. This is a challenge for recommender systems (RS) to face this setting. Online learning algorithms seem to be the most straight forward solution. The contextual bandit framework was introduced for that very purpose. In general the evaluation of a RS is a critical issue. Live evaluation is of- ten avoided due to the potential loss of revenue, hence the need for offline evaluation methods. Two options are available. Model based meth- ods are biased by nature and are thus difficult to trust when used alone. Data driven methods are therefore what we consider here. Evaluat- ing online learning algorithms with past data is not simple but some methods exist in the litera- ture. Nonetheless their accuracy is not satisfac- tory mainly due to their mechanism of data re- jection that only allow the exploitation of a small fraction of the data. We precisely address this issue in this paper. After highlighting the limita- tions of the previous methods, we present a new method, based on bootstrapping techniques. This new method comes with two important improve- ments: it is much more accurate and it provides a measure of quality of its estimation. The latter is a highly desirable property in order to minimize the risks entailed by putting online a RS for the first time. We provide both theoretical and ex- perimental proofs of its superiority compared to state-of-the-art methods, as well as an analysis of the convergence of the measure of quality

Allocation de ressources multi-débit pour la radio ULB impulsionnelle

National audienceDans cet article, nous considérons un système de communication multi-utilisateurs mettant en oeuvre une couche physique ultra-large bande (ULB) impulsionnelle à répartition par code. Nous nous intéressons tout d'abord à l'expression de la variance de l'interférence d'accès multiple (IAM) lorsque les utilisateurs ont des durées symboles différentes grâce à un nombre de trames, $N_f$, variable selon les utilisateurs. Nous nous intéressons ensuite au problème de la maximisation du débit global en affectant un nombre de trames différent pour chaque utilisateur sous contrainte de qualité de service (QoS) hétérogène pour certaines classes d'utilisateurs. Nous proposons une heuristique à complexité linéaire avec le nombre d'utilisateurs pour l'allocation du nombre de trames et évaluons ses performances par rapport à deux algorithmes de références

Affichage de publicités sur des portails web

International audienceNous nous intéressons au problème de l'affichage de publicités sur le web. De plus en plus d'annonceurs souhaitent maintenant payer uniquement lorsque quelqu'un clique sur leurs publicités. Dans ce modèle, l'opérateur du portail a intérêt à identifier les publicités les plus cliquées, selon ses catégories de visiteurs. Comme les probabilités de clic sont inconnues a priori, il s'agit d'un dilemme exploration/exploitation. Ce problème a souvent été traité en ne tenant pas compte de contraintes provenant du monde réel : les campagnes de publicités ont une durée de vie et possèdent un nombre de clics à assurer et ne pas dépasser. Pour cela, nous introduisons une approche hybride (MAB+LP) entre la program- mation linéaire et les bandits. Nos algorithmes sont testés sur des modèles créés avec un important acteur du web commercial. Ces expériences montrent que ces approches atteignent une performance très proche de l'optimum et mettent en évidence des aspects clés du problème

1947 Correspondence Between Lewiston Teachers Association and Louis-Philippe Gagné

Two letters of correspondence between Mary J. Hamilton, President of the Lewiston Teachers\u27 Association and Louis-Philippe Gagné. Letter 1: Letter from Mary J. Hamilton to Louis-Philippe Gagné inviting him and his wife to the Lewiston Teachers\u27 Association annual dinner. The letter is dated May 15, 1947. Letter 2: Louis-Philippe Gagné\u27s response to Mary J. Hamilton regarding the Lewiston Teachers\u27 Association annual dinner. The letter is dated May 20, 1947.https://digitalcommons.usm.maine.edu/fac-lpg-1947-04-06/1010/thumbnail.jp

ICML Exploration & Exploitation challenge: Keep it simple!

International audienceRecommendation has become a key feature in the economy of a lot of companies (online shopping, search engines...). There is a lot of work going on regarding recommender systems and there is still a lot to do to improve them. Indeed nowadays in many companies most of the job is done by hand. Moreover even when a supposedly smart recommender system is designed, it is hard to evaluate it without using real audience which obviously involves economic issues. The ICML Exploration & Exploitation challenge is an attempt to make people propose efficient recommendation techniques and particularly focuses on limited computational resources. The challenge also proposes a framework to address the problem of evaluating a recommendation algorithm with real data. We took part in this challenge and achieved the best performances; this paper aims at reporting on this achievement; we also discuss the evaluation process and propose a better one for future challenges of the same kind

Planning-based Approach for Optimizing the Display of Online Advertising Campaigns

In a realistic context, the online advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. Furthermore, receiving a click is usually a very rare event. Thus, the problem of choosing which advertisement to display on a web page is inherently dynamic, and intimately combines combinato- rial and statistical issues. We introduce a planning based algorithm for optimizing the display of advertisements and investigate its performance through simulations on a realistic model designed with an important commercial web actor