Bid Optimization in Broad-Match Ad auctions

Ad auctions in sponsored search support ``broad match'' that allows an advertiser to target a large number of queries while bidding only on a limited number. While giving more expressiveness to advertisers, this feature makes it challenging to optimize bids to maximize their returns: choosing to bid on a query as a broad match because it provides high profit results in one bidding for related queries which may yield low or even negative profits. We abstract and study the complexity of the {\em bid optimization problem} which is to determine an advertiser's bids on a subset of keywords (possibly using broad match) so that her profit is maximized. In the query language model when the advertiser is allowed to bid on all queries as broad match, we present an linear programming (LP)-based polynomial-time algorithm that gets the optimal profit. In the model in which an advertiser can only bid on keywords, ie., a subset of keywords as an exact or broad match, we show that this problem is not approximable within any reasonable approximation factor unless P=NP. To deal with this hardness result, we present a constant-factor approximation when the optimal profit significantly exceeds the cost. This algorithm is based on rounding a natural LP formulation of the problem. Finally, we study a budgeted variant of the problem, and show that in the query language model, one can find two budget constrained ad campaigns in polynomial time that implement the optimal bidding strategy. Our results are the first to address bid optimization under the broad match feature which is common in ad auctions.Comment: World Wide Web Conference (WWW09), 10 pages, 2 figure

Generalized Second Price Auction with Probabilistic Broad Match

Generalized Second Price (GSP) auctions are widely used by search engines today to sell their ad slots. Most search engines have supported broad match between queries and bid keywords when executing GSP auctions, however, it has been revealed that GSP auction with the standard broad-match mechanism they are currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g., its theoretical properties are known only for the single-slot case and full-information setting, and even in this simple setting, the corresponding worst-case social welfare can be rather bad). To address this issue, we propose a novel broad-match mechanism, which we call the Probabilistic Broad-Match (PBM) mechanism. Different from SBM that puts together the ads bidding on all the keywords matched to a given query for the GSP auction, the GSP with PBM (denoted as PBM-GSP) randomly samples a keyword according to a predefined probability distribution and only runs the GSP auction for the ads bidding on this sampled keyword. We perform a comprehensive study on the theoretical properties of the PBM-GSP. Specifically, we study its social welfare in the worst equilibrium, in both full-information and Bayesian settings. The results show that PBM-GSP can generate larger welfare than SBM-GSP under mild conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in Bayesian setting. To the best of our knowledge, this is the first work on broad-match mechanisms for GSP that goes beyond the single-slot case and the full-information setting

Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the incomplete information of historical advertising performance indices and the high uncertainty in SSA environments. First, we construct a data distribution estimation model and apply a Markov Chain Monte Carlo method to make inference about unobserved indices (i.e., impression and click-through rate) over three keyword matching types (i.e., broad, phrase and exact). Second, we formulate a stochastic keyword targeting model (BB-KSM) combining operations of keyword selection and keyword matching to maximize the expected profit under the chance constraint of the budget, and develop a branch-and-bound algorithm incorporating a stochastic simulation process for our keyword targeting model. Finally, based on a realworld dataset collected from field reports and logs of past SSA campaigns, computational experiments are conducted to evaluate the performance of our keyword targeting strategy. Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations; (c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions. This research makes important contributions to the SSA literature and the results offer critical insights into keyword management for SSA advertisers.Comment: 38 pages, 4 figures, 5 table

Програмна система управління інформаційними потоками в медійній рекламі

Кваліфікаційна робота включає пояснювальну записку (51 сторінку, 17 рисунків, 2 графіки) Об’єкт розробки – створення комп’ютерної системи автоматизації управління процесом продажу медійної реклами яка дозволяє завантажувати рекламні пропозиції та налаштовувати порядок та правила їх показу. Комп’ютерна система дозволяє: завантажувати медіа рекламу та забезпечувати їх збереження; створення рекламних кампаній, в основі яких лежать попередньо завантажені рекламні креативи; в автоматичному режимі вибрати оптимальну рекламну пропозицію з запропонованих рекламним запитом. В процесі розробки було використано стандарт OpenRTB версії 2.3. Всі описані сервіси було розроблено мовою програмування Node.js. В якості бази даних використовувалась MySQL. В ході розробки: − проведено аналіз стандарту OpenRTB; − сформульовані вимоги до комп’ютерної системи автоматизації управління продажу медійної реклами; − розроблена система автоматизації вибору оптимальної пропозиції з запропонованих варіантів рекламного запиту; − розроблено користувацький додаток для завантаження рекламних банерів та створення рекламних кампаній задля їх просування; − розроблено веб-сервіс для автоматичного завантаження даних створених через додаток та оперування цими даними для вибору оптимальної пропозиції; − розроблено програмне забезпечення для створення тестового навантаження на веб-сервіс.Qualification work includes explanatory note (51 page, 17 images, 2 graphs) Subject of development is creation of computer system that automates the selling process of advertisements and which handles creation if ad inventory and targeting rules. Developed computer system allows: to download ad banners and handles it’s storage; creation of ad campaigns, which are based on previously created ad banners; automatically select optimal ad position from provided ad request. Developed system is based on open-source standard OpenRTB version 2.3. All services were developed using Node.js. MySQL was used as main storage. During development process: − OpenRTB version 2.3 was analyzed ; − formulated requirements for the computer system that automates selling process of advertisements; − create automatization system for optimal deal selection from provided ad request; − developed user application for ad inventory and ad campaign creation; − developed web-service to handle automatic download of user provided data and using them to select an optimal deal; − Created software to perform load tests of main syste

Scheduling to minimize power consumption using submodular functions

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 59-64).We develop logarithmic approximation algorithms for extremely general formulations of multiprocessor multi-interval offline task scheduling to minimize power usage. Here each processor has an arbitrary specified power consumption to be turned on for each possible time interval, and each job has a specified list of time interval/processor pairs during which it could be scheduled. (A processor need not be in use for an entire interval it is turned on.) If there is a feasible schedule, our algorithm finds a feasible schedule with total power usage within an O(log n) factor of optimal, where n is the number of jobs. (Even in a simple setting with one processor, the problem is Set-Cover hard.) If not all jobs can be scheduled and each job has a specified value, then our algorithm finds a schedule of value at least (1 - c)Z and power usage within an O(log(1/E)) factor of the optimal schedule of value at least Z, for any specified Z and c > 0. At the foundation of our work is a general framework for logarithmic approximation to maximizing any submodular function subject to budget constraints. We also introduce the online version of this scheduling problem, and show its relation to the classical secretary problem. In order to obtain constant competitive algorithms for this online version, we study the secretary problem with submodular utility function. We present several constant competitive algorithms for the secretary problem with different kinds of utility functions.by Morteza Zadimoghaddam.S.M

Optimisation du positionnement des annonces textuelles en marketing interactif

RÉSUMÉ : Lorsqu’un utilisateur tape du texte dans le champ réservé à cet effet dans un moteur de recherche, pendant le chargement de la page des résultats, une enchère a lieu pour déterminer quelles annonces textuelles seront affichées et dans quel ordre. Le texte tapé par l’utilisateur doit pouvoir être associé à au moins un des mots-clés choisis par un annonceur pour que ce dernier soit éligible à participer à cette enchère. Plus une annonce est affichée haut sur la page, mieux elle est classée et; mieux elle est classée, plus l’annonceur devra payer pour chaque clic sur ladite annonce et plus les utilisateurs cliqueront dessus. Toutefois, le montant facturé par le moteur de recherche à un annonceur pour chaque clic ne dépasse jamais l’enchère de cet annonceur. De plus, l’annonceur peut fixer un budget périodique (par jour, semaine ou mois) que le moteur de recherche devra respecter. Les annonceurs doivent donc établir une stratégie d’enchère qui leur permettra de maximiser la performance de leurs campagnes tout en respectant leur budget. Malheureusement, ils n’ont accès qu’à une quantité limitée d’informations pour le faire. Dans le cadre de ce projet de recherche, un modèle d’optimisation est proposé. Il permet de déterminer la meilleure position pour chaque mot-clé avec pour objectif d’optimiser les profits générés par une ou plusieurs campagnes d’un annonceur ou d’autres indices de performance de campagne tels que les revenus ou les conversions. Ce modèle, présenté dans le troisième chapitre, est basé sur les données de navigation partielle des utilisateurs. Elles sont représentées par un graphe et le problème est formulé comme un problème de flot ou de flot à coût minimum selon l’objectif utilisé. Le modèle n’étant pas linéaire, nous avons également développé cinq algorithmes pour résoudre ce problème. Quatre d’entre eux sont basés sur l’algorithme tabou. Dans la version élémentaire de l’algorithme, on modifie d’une unité la position d’un mot-clé et on recalcule le flot en utilisant des formules liant la position d’un mot-clé au nombre de clics espérés sur celui-ci ainsi qu’au coût-par-clic moyen de ce mot-clé. Le mot-clé dont la position est changée est choisi de sorte que la modification soit la plus profitable possible tout en respectant les contraintes de budget. Par la suite, il devient tabou de modifier à nouveau la position du même mot-clé pendant un nombre prédéterminé d’itérations. Les trois autres algorithmes sont des variantes du premier. Dans la première variante, la position peut être modifiée de plus d’une unité. Dans la seconde, on accepte avec une pénalité que le budget soit dépassé. Finalement, dans la troisième version, les variantes des deux premières versions sont implémentées. Le cinquième algorithme est basé sur l’algorithme glouton. À chaque itération, pour chaque mot-clé, on détermine s’il est préférable de maintenir sa position ou de la modifier d’une unité. Si changer de position est plus profitable, alors on détermine quelle position est la plus profitable et on effectue le changement. Ce changement peut être de plus d’une unité. Les dépassements de budget sont permis mais pénalisés. Cet algorithme s’est avéré être le plus rapide avec des résultats comparables à ceux obtenus avec les tabous. Dans le quatrième chapitre, on propose une étude comparative sur l’impact de différentes modifications, celles-ci étant apportées soit au modèle soit à un paramètre. La première étude consiste à modifier la fonction-objectif et de mesurer l’impact de ce changement sur les résultats. Nous avons comparé les valeurs de différentes mesures de performances lorsque l’objectif est d’optimiser une d’elles à la fois. L’analyse révèle que le profit et les coûts sont les plus sensibles au changement et que la maximisation des visites est l’objectif ayant l’impact le plus négatif sur les autres mesures de performance. Une seconde étude analyse l’impact de la variation du budget sur les résultats de la campagne. Dans cette étude, on constate que la maximisation du profit est peu sensible à la variation de budget et que, pour les autres objectifs, une variation de ±10% ou 20% ne se traduit pas nécessairement en une variation de performance de ±10% ou 20%. Finalement, la dernière modification analysée est la variation du coût-par-clic sur l’ensemble des mots-clés. Pour cette analyse, les coûts-par-clic de tous les mots-clés sont aléatoirement modifiés afin de voir si ces changements ont un impact significatif sur les valeurs des indices de performance et sur les positions des mots-clés. Dans le cas de la maximisation des profits, moins de 7% des mots-clés se sont retrouvé à des positions différentes au final et la différence était seulement d’une unité. Pour le reste des objectifs, les variations sont plus importantes.----------ABSTRACT : When a user types a text in the search field of a search engine, during the loading of the page, an auction takes place to determine which text ads will appear along with the search results and in which order. The text typed by the user must be associated with at least one of the keywords chosen by an advertiser for that advertiser to be eligible to participate in that auction. In this thesis, we only consider the first ten positions which are all on the first page as all the others have very little impact on the ad performance. So, the highest an ad is placed on the result page, the better it is ranked, the more each click will cost and the more clicks it will receive. However, the cost-per-click billed to the advertiser never exceeds his bid (the maximal amount he is willing to pay per click). Moreover, the advertiser can set a periodical budget. A period might be a day, a week or a month. That budget is to be respected by the search engine. The advertiser must find the best bidding strategy that will help them maximize the performance of their ad campaigns while respecting their budget. Thus, in the third chapter, a new model to determine which position to aim for on search engines using partial users’ navigation history available to advertisers is proposed. For a given advertiser, we represent as a graph the partial navigation history of users who interacted with any element of a campaign such as clicking on text ads or banners, visiting a page of the advertiser’s website containing a web tracker, etc. We modeled the problem assuming that an increase of traffic at one vertex implies a decrease for all vertices sharing the same parent(s) and vice versa. Based on that, we reorganize the flow within the graph to maximize the expected profits from conversions tracked online. To solve the problem, we designed five algorithms. Four of them are based on the tabu search algorithm. In the most basic one, we change the position of one keyword by one unit then recalculate the flow using the functions mentioned earlier. The keyword which position is to be changed is chosen so that the change is the most profitable but respects the budget. Changing the position of that keyword then becomes tabu for a given number of iterations. The three other tabu algorithms are variations of the basic one. In the first variation, we allow the position of the chosen keyword to be changed by more than one unit. In the second, the change can lead to expenses greater than the budget. In the last one, both variations are combined. The fifth algorithm is a greedy one. During every iteration, for each keyword, we determine whether keeping it at the same position or changing its position by one unit is more profitable. If changing is chosen, we then determine the number of units that makes the change the most profitable. Exceeding the budget is allowed with a penalty. This algorithm turned out to be the fastest with results similar in value to those of the tabu algorithms. The profit of a campaign is only one of the possible measures of its efficiency. In our second article, we analyze how different changes impact the campaign management by comparing the results obtained with different objective-functions. The first change analyzed was the use of a different objective-function. We compared the values of the performance indicators obtained when optimizing each one of them. The analysis revealed that the profit and the costs are the most sensitive to change. Also, visits maximization has the most negative impact than other objective-functions on performance indicators other than visits. The second change we tested was the variation of budget. Profit maximization was not very sensitive to that. For the other objective-functions, it had more impact but ±10% or 20% did not necessarily translate to a variation of ±10% or 20% in value for any of them. The last change was the variation of the cost-per-click. We randomly changed the cost-perclick of every keyword and compared the values of the performance indicators before and after the change. For profit maximization, less than 7% of the keywords saw their best positions change by one unit and the positions of other keywords did not change at all. The impact was more significant for the other objective-functions