Inter Process Communication and Prioritization to Enable Desktop Advertisement Mechanism

This research paper tries to bring in a new concept of desktop advertising mechanism by synchronization it with the running processes and the data on users’ side. The proposed approach shall be based on inter process communication interaction, scheduling, prioritization, desktop crawling and system calls. The running process status and data will be fetched by the proposed process, which will then seek relevant information with the remote ad server and display the advertisements fetched based on keywords on user side

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

Advertising Campaigns Management: Should We Be Greedy?

International audienceWe consider the problem of displaying commercial advertisements on web pages, in the "cost per click" model. The advertisement server has to learn the appeal of each type of visitors for the different advertisements in order to maximize the revenue. In a realistic context, the advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. This problem is thus inherently dynamic, and intimately combines combinatorial and statistical issues. To set the stage, it is also noteworthy that we deal with very rare events of interest, since the base probability of one click is in the order of 10−4 . Different approaches may be thought of, ranging from computationally demanding ones (use of Markov decision processes, or stochastic programming) to very fast ones. We introduce noseed, an adaptive policy learning algorithm based on a combination of linear programming and multi-arm bandits. We also propose a way to evaluate the extent to which we have to handle the constraints (which is directly related to the computation cost). We investigate performance of our system through simulations on a realistic model designed with an important commercial web actor.Nous nous intéressons au problème de la sélection de messages publicitaires sur des pages web dans le modèle de paiement au clic. Pour cela, le serveur doit apprendre l'appétance de chaque type de visiteurs pour les différentes publicités en stock afin de maximiser ses revenus. Dans un contexte réaliste, les publicités possèdent des contraintes telles qu'un nombre de clics à obtenir et une durée de vie. Ce problème est dynamique et combine intimement des aspects combinatoires et statistiques~; de plus, il est important de noter que nous considérons des événements rares, la probabilité de clic de base étant de l'ordre de $10^{-4}$. Différentes approches peuvent etre envisagées, allant d'approches extrêmement gourmandes en temps de calcul (en utilisant des processus décisionnel de Markov ou une formulation de type programmation stochastique) à des approches très rapides. Nous introduisons \algo{} qui est un algorithme adaptatif d'apprentissage de politique basé sur une combinaison de programmation linéaire et de bandits multi-bras. Nous proposons également une manière d'évaluer les contraintes à satisfaire, ce qui est directement relié au coût en temps de calcul. Nous investiguons les performances de notre algorithme dans un modèle réaliste conçu avec un important acteur du web commercial