By Subodha Kumar
ISBN-10: 3319186442
ISBN-13: 9783319186443
ISBN-10: 3319186450
ISBN-13: 9783319186450
This ebook presents a finished evaluate of optimization matters and types in net and cellular ads. It starts by way of discussing the evolution of online advertising over the years. this is often via the dialogue of popular pricing versions. The reader is supplied with a uncomplicated evaluate of other optimization matters focused on online advertising. the sooner versions quite often thought of the matter of scheduling advertisements competing to be put on an internet web page. right here, the advertisements have been laid out in geometry and show frequency, and either one of those elements have been thought of in constructing an answer to the commercial scheduling challenge. those versions have been comparable in nature to the matter of scheduling advertisements on newspaper or tv, however the pricing constitution in those versions have been diverse from these in newspaper or tv advertisements. because the online advertising advanced, the preliminary types have been augmented via contemplating how the agenda of advertisements is modified in keeping with person person click on habit. therefore, those versions thought of personalization in online advertising. The e-book additionally offers ways to aid resolve those types. extra lately, there was great development in cellular advertisements. This ebook additionally presents the main points of commercial version in cellular ads, and offers ideas for the optimization challenge taken with cellular ads. also this ebook seems to be to key destiny developments in net and cellular advertisements (such as Fading Ads) and the associat
ed demanding situations that include it. for example, the long run tendencies in pricing versions are extra in the direction of action-based pricing instead of impression-based pricing.
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Additional info for Optimization Issues in Web and Mobile Advertising: Past and Future Trends
Example text
However, tracking this advantage from one slot to the next is the crux of the issue. Based on the above discussion, in [6], the authors propose the following three hypotheses: Hypothesis 1 If p < min i min {k:eik >ei(k+1) } (eik − ei(k+1) ), and there are sufficient ads available so that it is possible to fill all the slots without repeating an ad, then the expected revenue is the same in the static and dynamic versions. , Ci(k+1) > C j1 , ∀ j = i, ∀k), and there are sufficient ads available so that it is possible to fill all the slots without repeating an ad, then the difference in the expected revenue between the dynamic and static versions increases or remains the same with an increase in ai1 .
This U-shaped functional form has been observed in recent research on individual clickstream analysis using data from a commercial web site and is different from the inverted U-shaped response found in traditional broadcast media. 1) where ai0 , ai1 and bi1 are positive constants with ai1 >> bi1 . • re-click effect: A user clicking on an ad during a visit increases her probability of clicking on that ad in future exposures during the same visit. Thus, if an ad has been clicked upon, the re-click effect increases the click probability by an amount p in all subsequent exposures of the ad during the current visit.
Moreover, the dynamic revenue is always greater than the static revenue at any error level. Therefore, we conclude that it is always better to use the dynamic schedule even when we do not have a good estimate of the value of p. Also, it is better to overestimate rather than underestimate the value of p. Although the look-ahead dynamic approach outperforms the static approach, one practical concern that is relevant is the time needed to execute the dynamic approach. Note that while the static approach computes the entire schedule in advance, the look-ahead dynamic approach must recompute an updated schedule after every slot.
Optimization Issues in Web and Mobile Advertising: Past and Future Trends by Subodha Kumar
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