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Advertisers in online social networks (OSNs) like Facebook and LinkedIn have some preferred set of users they wish to reach by showing their ads. OSNs offer fine-grained sets of user characteristics — including their career, wealth, education information, etc — that advertisers can specify for targeting their audience, and each of these characteristics requires different amounts of money for targeting...
A distributed architecture for implementing online social networks (OSNs) can overcome several disadvantages of the now popular centralized online social networks such as Face book or Twitter. Owners of centralized OSNs control all of the individuals' data and associated policies on dissemination of data. Hence, individuals can hardly exert control of their personal data, resulting in serious potential...
Internet search companies sell advertisement slots based on users’ search queries via an auction. Advertisers have to determine how to place bids on the keywords of their interest in order to maximize their return for a given budget: this is the budget optimization problem. The solution depends on the distribution of future queries. In this paper, we formulate stochastic versions of the budget optimization...
We study the online stochastic bipartite matching problem, in a form motivated by display ad allocation on the Internet. In the online, but adversarial case, the celebrated result of Karp, Vazirani and Vazirani gives an approximation ratio of 1- 1/e ?? 0.632, a very familiar bound that holds for many online problems; further, the bound is tight in this case. In the online, stochastic case when nodes...
We study the basic problem of preemptive scheduling of a stream of jobs on a single processor. Consider an on-line stream of jobs, and let the ith job arrive at time r(i) and have processing time p(i). If C(i) is the completion time of job i, then the flow time of i is C(i) − r(i) and the stretch of i is the ratio of its flow time to its processing time; that is, $$\frac{{C(i) - r(i)}}{{p(i)}}$$...
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