In particular, there are a few spatio-temporal data models for representing gradual changes. An application scenario in the tourism domain and a Web-based Java prototype provide an experimental validation of the research framework and identified personalization techniques. We also take a software project as the application scenario to illustrate our model and the tracking process identified. Gradual changes are represented at different levels of abstraction while spatio-temporal predicates manipulate the modeling primitives identified. Our user-modeling approach is exemplarily applied to the tourism domain, to describe user information and identify user's classification. Huang Optimal auctions for transportation service procurement in an infinite horizon inventory system , 2014. Research and analytics cookies These cookies help us understand user behavior within our services.
Sponsored search auctions have been acknowledged as one of the most effective marketing vehicles available on the Web. Given a limited budget on a search market during a certain period, an advertiser has to distribute her budget to a series of sequential temporal slots i. Perspectives: Looking into the Future of Budgeting Strategies in Sponsored Search Auctions. Budget Planning for Coupled Campaigns in Sponsored Search Auctions 13. We also study some desirable properties and possible solutions to our budget model. In cost-sensitive learning, misclassification costs can vary for different classes. With serious advertising budget constraints, advertisers have to adjust their daily budget according to the performance of advertisements in real time.
The logic-based user-modeling framework acts as a support for a semantic Web personalization system to tailor information services to the user. Stochastic Budget Strategies at the CompaignLevel: A Preliminary Investigation 10. The user communities involved in spatial web services are essentially diverse, still in an expansion and transformation with constantly increasing number of user and applications. Experimental results show that our method can approach the optimal solution in a steady way, and it outperforms two baseline keyword strategies commonly used in practice. In search advertisements, advertisers have to seek for an effective allocation strategy to distribute the limited budget over a series of sequential temporal slots e. This gives rise to a bipartite matching market that is typically cleared by the way of an automated auction.
We propose a stochastic, risk-constrained budget strategy, considering a random factor of clicks per unit cost to capture a kind of uncertainty at the campaign level. There exists a budget demand for each temporal slot, which can not be known exactly by the advertiser due to some uncertainties in the search marketing environments. This information helps us design a better experience for all users. A Stochastic Budget Distribution Model in Search Advertisements 11. This paper presents a novel reduction framework free of such restrictions, in this sense, which is more efficient and versatile in real world applications.
In this paper, we present a stochastic model for budget distribution over a series of sequential temporal slots under a finite time horizon, assuming that the best budget is a random variable. The proposed model captures the dynamic nature of the search advertising market by incorporating two important advertising variables, namely, the time-varying quality score and the potential market revenue of advertisers, which are specific to the unique context of sponsored search advertising. This demonstrates the applicability of the clustering scheme and our algorithm in practice. Computational experiments are made to evaluate our model and identified properties. Search engines are designed to help Web users search for useful and relevant information on the Web.
This paper provides a method for planing the optimal daily budget in the online sponsored search advertising market, which is now the most important marketing place. Reference locations are possible locations from where the user can act in the city. This opens many research challenges, such as the elicitation of user's interests and preferences and customization of information services on the spatial Web. Such uniform strategies are likely to be practical. In this paper, we formulate stochastic versions of the budget optimization problem based on natural probabilistic models of distribution over future queries, and address two questions that arise.
In one involving Coca-Cola and Pepsi-Cola, a formal statistical testing procedure is used to detect whether closed-loop equilibrium advertising strategies are used by the competitors rather than open-loop strategies. The user model employs both static and dynamic user information to predict user features relevant to a given application domain. However, due to the heterogeneity of major search markets in terms of auction mechanisms, ranking algorithms, and advertising structures, it is becoming increasingly difficult for an advertiser to manipulate advertising budget simultaneously across several search markets. Searches cannot start with a wildcard. A three-dimensional measure of substitution relationships between campaigns is presented, namely, the overlapping degree in terms of campaign contents, promotional periods, and target regions.
However, millions of advertisers with serious budget constraints aggravate the competitive status in sponsored search auctions, and budget constraints define the strategy space and thus affect possible equilibrium solutions. Harry -Potter Search results will not contain 'Potter'. The spatial component considers contextual distances between places and reference locations. The series' readership is broad, but focuses on engineering, electronics, and computer science. The Intelligent Systems Series publishes reference works and handbooks in three core sub-topic areas: Intelligent Automation, Intelligent Transportation Systems, and Intelligent Computing. Our main result is that, un-der a reasonable assumption that online advertising has positive carryover effects on the propensity and the form of user inter-actions with the same advertiser in the future, there is a simple greedy algorithm for the budget allocation with the worst-case running time cubic in the number of model states potential ad-vertising keywords and an efficient parallel implementation in a distributed computing framework like MapReduce.
The main results can be summarized as follows: 1 In the case of complete-information game settings, there exist Nash equilibrium strategies for budget allocation; 2 while in the case of incomplete-information game settings, there exist Bayesian Nash equilibrium strategies for budget allocation. Budget constraints and optimization in sponsored search auctions. We algorithmically classify queries into states and then use n-grams to categorize search patterns. Using a specificaction that is consistent with empirical observations, we show that, in the long run, an advertiser must always spend more ad money at the specialized portal. Another interesting finding suggests that advertisers should set a budget cap even in a competitive environment to avoid the potential risk of over-spending in sponsored search advertising. Our analysis shows that an advertisers optimal portal budgeting depends nonlinearly on the number of visitors who type the same trigger keyword and the average clicksthrough rates, as well as on the advertiser and ad effectiveness.
. Two empirical applications are conducted. Sponsored search auctions have now become a popular online advertising format, thus being acknowledged as a promising business model for search engines. Given the competitive and dynamic nature of the search advertising market, it has long been a great challenge for advertisers to identify their optimal advertising strategies. Rousu, Are Experimental Auctions Demand Revealing when Values are Affiliated? We define a model of social relationships based on a set of call detail record factors of telecom users and design a vacancy-filling method to reduce the scarcity of the user—product matrix. Furthermore, we conductexperiments with the real-world data of voice calls, and experimental results show that the filling method proposed can effectively reduce the scarcity of the user—product matrix and our social relationships approach outperforms the collaborative filtering in terms of the call, precision and mean absolute error indicators.