1,047 research outputs found
Optimizing Your Online-Advertisement Asynchronously
We consider the problem of designing optimal online-ad investment strategies
for a single advertiser, who invests at multiple sponsored search sites
simultaneously, with the objective of maximizing his average revenue subject to
the advertising budget constraint. A greedy online investment scheme is
developed to achieve an average revenue that can be pushed to within
of the optimal, for any , with a tradeoff that the
temporal budget violation is . Different from many existing
algorithms, our scheme allows the advertiser to \emph{asynchronously} update
his investments on each search engine site, hence applies to systems where the
timescales of action update intervals are heterogeneous for different sites. We
also quantify the impact of inaccurate estimation of the system dynamics and
show that the algorithm is robust against imperfect system knowledge
Supply Side Optimisation in Online Display Advertising
On the Internet there are publishers (the supply side) who provide free contents (e.g., news) and services (e.g., email) to attract users. Publishers get paid by selling ad displaying opportunities (i.e., impressions) to advertisers. Advertisers then sell products to users who are converted by ads. Better supply side revenue allows more free content and services to be created, thus, benefiting the entire online advertising ecosystem. This thesis addresses several optimisation problems for the supply side. When a publisher creates an ad-supported website, he needs to decide the percentage of ads first. The thesis reports a large-scale empirical study of Internet ad density over past seven years, then presents a model that includes many factors, especially the competition among similar publishers, and gives an optimal dynamic ad density that generates the maximum revenue over time. This study also unveils the tragedy of the commons in online advertising where users' attention has been overgrazed which results in a global sub-optimum. After deciding the ad density, the publisher retrieves ads from various sources, including contracts, ad networks, and ad exchanges. This forms an exploration-exploitation problem when ad sources are typically unknown before trail. This problem is modelled using Partially Observable Markov Decision Process (POMDP), and the exploration efficiency is increased by utilising the correlation of ads. The proposed method reports 23.4% better than the best performing baseline in the real-world data based experiments. Since some ad networks allow (or expect) an input of keywords, the thesis also presents an adaptive keyword extraction system using BM25F algorithm and the multi-armed bandits model. This system has been tested by a domain service provider in crowdsourcing based experiments. If the publisher selects a Real-Time Bidding (RTB) ad source, he can use reserve price to manipulate auctions for better payoff. This thesis proposes a simplified game model that considers the competition between seller and buyer to be one-shot instead of repeated and gives heuristics that can be easily implemented. The model has been evaluated in a production environment and reported 12.3% average increase of revenue. The documentation of a prototype system for reserve price optimisation is also presented in the appendix of the thesis
Recommended from our members
Online learning and decision-making from implicit feedback
This thesis focuses on designing learning and control algorithms for emerging resource allocation platforms like recommender systems, 5G wireless networks, and online marketplaces. These systems have an environment which is only partially known. Thus, the controllers need to make resource allocation decisions based on implicit feedback obtained from the environment based on past actions. The goal is to sequentially select actions using incremental feedback so as to optimize performance while simultaneously learning about the environment. We study three problems which exemplify this setting. The first is an inference problem which requires identification of sponsored content in recommender systems. Specifically, we ask if it is possible to detect the existence of sponsored content disguised as genuine recommendations using implicit feedback from a subset of users of the recommender system. The second problem is the design of scheduling algorithms for switch networks when the user-server link statistics are unknown (for e.g., in wireless networks, online marketplaces). The scheduling algorithm has to tradeoff between scheduling the optimal links and obtaining sufficient feedback about all the links for accurate estimates. We observe the close connection of this problem to the stochastic multi-armed bandit problem and analyze bandit-style explore-exploit algorithms for learning the statistical parameters while simultaneously assigning servers to users. The third is the joint problem of base station activation and rate allocation in an energy efficient wireless network when the channel statistics are unknown. The controller observes instantaneous channel rates of activated BSs, and thereby sequentially obtains implicit feedback about the channel. Here again, there is a tradeoff between learning the channel versus optimizing the operation cost based on estimated parameters. For each of these systems, we propose algorithms with provable asymptotic guarantees. These learning algorithms highlight the use of implicit feedback in online decision making and control.Electrical and Computer Engineerin
07471 Abstracts Collection -- Equilibrium Computation
From 18 to 23 November 2007, the Dagstuhl Seminar 07471 ``Equilibrium Computation\u27\u27 was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Entropy-Based Dynamic Ad Placement Algorithms for In-Video Advertising
With the evolution of the Internet and the increasing number of users over last years, online
advertising has become one of the pillars models that sustains many of the Internet businesses.
In this dissertation, we review the history of online advertising, will be made, as well as the
state-of-the-art of the major scientific contributions in online advertising,in particularly in
respect to in-video advertising.
In in-video advertising, one of the major issues is to identify the best places for insertion of
ads. In the literature, this problem is addressed in different ways. Some methods are designed
for a specific genres of video, e.g., football or tennis, while others are independent of genre,
trying to identify the meaningful video scenes (a set of continuous and related frames) where
ads will be displayed.
However, the vast majority of online videos in the Internet are not long enough to identify
large scenes. So, in this dissertation we will address a new solution for advertisement insertion
in online videos, a solution that can be utilized independently of the duration and genre of the
video in question.
When developing a solution for in-video advertising, a major challenge rests on the intrusiveness
that the ad inserted will take upon the viewer. The intrusiveness is related to the place and
timing used by the advertising to be inserted. For these reasons, the algorithm has to take in
consideration the "where", "when" and "how" the advertisement should be inserted in the video,
so that it is possible to reduce the intrusiveness of the ads to the viewer.
In short, in addition to besides being independent of duration and genre, the proposed method
for ad placement in video was developed taking in consideration the ad intrusiveness to the
user.Com a evolução da Internet e o nĂșmero crescente de utilizadores ao longo destes Ășltimos anos,
a publicidade on-line tornou-se um dos modelos base que tem sustentado muitos negĂłcios na
Internet. Da mesma forma, vĂdeos on-line constituem uma parte significativa do trĂĄfego na
Internet. Ă por isso possĂvel entender desta forma, o potencial que ferramentas que possĂŁo
explorar eficientemente ambas estas ĂĄreas possuem no mercado.
Nesta dissertação serå feita uma revisão da história da publicidade online, mas também serå
apresentado ao leitor uma revisĂŁo sobre o estado da arte das principais contribuiçÔes cientĂficas
para a publicidade on-line, em especial para a publicidade em video.
Na publicidade em vĂdeo, uma das principais preocupaçÔes Ă© identificar os melhores locais para
a inserir os anĂșncios. Na literatura, este problema Ă© abordado de diferentes maneiras, alguns
criaram mĂ©todos para gĂȘneros especĂficos de vĂdeo, por exemplo, futebol ou tĂ©nis, outros
mĂ©todos sĂŁo independentes do gĂȘnero, mas tentam identificar as cenas de vĂdeo (um conjunto
contĂnuo de frames relacionadas) e apenas exibir anĂșncios neles.
No entanto, a grande maioria dos vĂdeos on-line na Internet nĂŁo sĂŁo suficiente longos para serem
identificadas cenas suficientemente longas para inserir os anĂșncios. Assim, nesta dissertação
iremos abordar uma nova solução para a inserção de anĂșnicios em vĂdeos, uma solução que
pode ser utilizada de forma independente da duração e gĂȘnero do vĂdeo em questĂŁo.
Ao desenvolver uma solução para inserir anĂșncos em vĂdeos a grande preocupação recai sobre
a intromissĂŁo que o anĂșncio inserido poderĂĄ ter sobre o utilizador. A intrusĂŁo estĂĄ relacionada
com o local e tempo utilizado pela publicidade quando é inserida. Por estas razÔes, o algoritmo
tem que levar em consideração "onde", "quando" e "como" o anĂșncio deve ser inserido no vĂdeo,
de modo que seja possĂvel reduzir a intromissĂŁo dos anĂșncios para o utilizador.
Em suma, para alĂ©m de ser independente da duração e gĂȘnero do vĂdeo, o mĂ©todo proposto
serĂĄ tambĂ©m desenvolvido tendo em consideração a intromissĂĄo do anĂșncio para o utilizador.
Por fim, o método proposto serå testado e comparado com outros métodos, de modo a que seja
possivel perceber as suas capacidades
Optimizing trade-offs among stakeholders in real-time bidding by incorporating multimedia metrics
Displaying banner advertisements (in short, ads) on webpages has usually been discussed as an Internet economics topic where a publisher uses auction models to sell an online user's page view to advertisers and the one with the highest bid can have her ad displayed to the user. This is also called \emph{real-time bidding} (RTB) and the ad displaying process ensures that the publisher's benefit is maximized or there is an equilibrium in ad auctions. However, the benefits of the other two stakeholders -- the advertiser and the user -- have been rarely discussed. In this paper, we propose a two-stage computational framework that selects a banner ad based on the optimized trade-offs among all stakeholders. The first stage is still auction based and the second stage re-ranks ads by considering the benefits of all stakeholders. Our metric variables are: the publisher's revenue, the advertiser's utility, the ad memorability, the ad click-through rate (CTR), the contextual relevance, and the visual saliency. To the best of our knowledge, this is the first work that optimizes trade-offs among all stakeholders in RTB by incorporating multimedia metrics. An algorithm is also proposed to determine the optimal weights of the metric variables. We use both ad auction datasets and multimedia datasets to validate the proposed framework. Our experimental results show that the publisher can significantly improve the other stakeholders' benefits by slightly reducing her revenue in the short-term. In the long run, advertisers and users will be more engaged, the increased demand of advertising and the increased supply of page views can then boost the publisher's revenue
- âŠ