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 $O(\epsilon)$ of the optimal, for any $\epsilon>0$, with a tradeoff that the temporal budget violation is $O(1/\epsilon)$. 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

The Value-of-Information in Matching with Queues

We consider the problem of \emph{optimal matching with queues} in dynamic systems and investigate the value-of-information. In such systems, the operators match tasks and resources stored in queues, with the objective of maximizing the system utility of the matching reward profile, minus the average matching cost. This problem appears in many practical systems and the main challenges are the no-underflow constraints, and the lack of matching-reward information and system dynamics statistics. We develop two online matching algorithms: Learning-aided Reward optimAl Matching ($\mathtt{LRAM}$) and Dual-$\mathtt{LRAM}$ ($\mathtt{DRAM}$) to effectively resolve both challenges. Both algorithms are equipped with a learning module for estimating the matching-reward information, while $\mathtt{DRAM}$ incorporates an additional module for learning the system dynamics. We show that both algorithms achieve an $O(\epsilon+\delta_r)$ close-to-optimal utility performance for any $\epsilon>0$, while $\mathtt{DRAM}$ achieves a faster convergence speed and a better delay compared to $\mathtt{LRAM}$, i.e., $O(\delta_{z}/\epsilon + \log(1/\epsilon)^2))$ delay and $O(\delta_z/\epsilon)$ convergence under $\mathtt{DRAM}$ compared to $O(1/\epsilon)$ delay and convergence under $\mathtt{LRAM}$ ($\delta_r$ and $\delta_z$ are maximum estimation errors for reward and system dynamics). Our results reveal that information of different system components can play very different roles in algorithm performance and provide a systematic way for designing joint learning-control algorithms for dynamic systems

AN ENSEMBLE MODEL FOR CLICK THROUGH RATE PREDICTION

Internet has become the most prominent and accessible way to spread the news about an event or to pitch, advertise and sell a product, globally. The success of any advertisement campaign lies in reaching the right class of target audience and eventually convert them as potential customers in the future. Search engines like the Google, Yahoo, Bing are a few of the most used ones by the businesses to market their product. Apart from this, certain websites like the www.alibaba.com that has more traffic also offer services for B2B customers to set their advertisement campaign. The look of the advertisement, the maximum bill per day, the age and gender of the audience, the bid price for the position and the size of the advertisement are some of the key factors that are available for the businesses to tune. The businesses are predominantly charged based the number of clicks that they received for their advertisement while some websites also bill them with a fixed charge per billing cycle. This creates a necessity for the advertising platforms to analyze and study these influential factors to achieve the maximum possible gain through the advertisements. Additionally, it is equally important for the businesses to customize these factors rightly to achieve the maximum clicks. This research presents a click through rate prediction system that analyzes several of the factors mentioned above to predict if an advertisement will receive a click or not with improvements over the existing systems in terms of the sampling the data, the features used, and the methodologies handled to improve the accuracy. We used the ensemble model with weighted scheme and achieved an accuracy of 0.91 on a unit scale and predicted the probability for an advertisement to receive a click form the user

Efficient advert assignment

We develop a framework for the analysis of large-scale ad auctions where adverts are assigned over a continuum of search types. For this pay-per-click market, we provide an efficient mechanism that maximizes social welfare. In particular, we show that the social welfare optimization can be solved in separate optimizations conducted on the time scales relevant to the search platform and advertisers. Here, on each search occurrence, the platform solves an assignment problem and, on a slower time scale, each advertiser submits a bid that matches its demand for click-throughs with supply. Importantly, knowledge of global parameters, such as the distribution of search terms, is not required when separating the problem in this way. Exploiting the information asymmetry between the platform and advertiser, we describe a simple mechanism that incentivizes truthful bidding, has a unique Nash equilibrium that is socially optimal, and thus implements our decomposition. Further, we consider models where advertisers adapt their bids smoothly over time and prove convergence to the solution that maximizes social welfare. Finally, we describe several extensions that illustrate the flexibility and tractability of our framework.The research of Neil Walton was funded by the VENI research programme, which is financed by the Netherlands Organisation for Scientific Research (NWO)