Learning to advertise

Lacerda, Anísio; Cristo, Marco; Gonçalves, Marcos André; Fan, Weiguo; Ziviani, Nívio; Ribeiro-Neto, Berthier

doi:10.1145/1148170.1148265

Cited by 129 publications

(69 citation statements)

References 18 publications

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“…To put the sparsity into perspective, in the literature researchers have often found it to be difficult to predict clicks where the click-through rates are very small (e.g. 0.01 for certain applications) [3,8,11]. Conversions are usually two to three orders of magnitude rarer than clicks.…”

Section: Resultsmentioning

confidence: 99%

Retrieval models for audience selection in display advertising

Tyler

Pandey

Gabrilovich

et al. 2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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Web applications often rely on user profiles of observed user actions, such as queries issued, page views, etc. In audience selection for display advertising, the audience that is likely to be responsive to a given ad campaign is identified via such profiles. We formalize the audience selection problem as a ranked retrieval task over an index of known users. We focus on the common case of audience selection where a small seed set of users who have previously responded positively to the campaign is used to identify a broader target audience. The actions of the users in the seed set are aggregated to construct a query, the query is then executed against an index of other user profiles to retrieve the highest scoring profiles. validate our approach on a real-world dataset, demonstrating the trade-offs of different user and query models and find that our approach is particularly robust for small campaigns. The proposed user modeling framework is applicable to many other applications requiring user profiles such as content suggestion and personalization.

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Section: Resultsmentioning

confidence: 99%

Retrieval models for audience selection in display advertising

Tyler

Pandey

Gabrilovich

et al. 2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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show abstract

“…The main advantage of Genetic Programming [2] approach is that it provides a very suitable manner to find the duplicate data very efficiently without searching the entire search space for a solution which may be very large. Till now GP has been applied in many areas of information handling like Ranking Function Discovery [3], Document Classification [5], Content Based Image Retrieval [11] and content target Advertising [6].…”

Section: Genetic Algorithm Based Modelmentioning

confidence: 99%

A Brief Review Along With a New Proposed Approach of Data De Duplication

Saha¹,

Samanta²

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Complementary approaches have been reported by Lacerda et al [9] and by Ribeiro-Neto et al [16]. Lacerda et al focused on the selection of good ranking functions for matching ads to pages, using genetic programming for generating a non-linear combination of term weighting heuristics that maximizes the average precision on retrieving ads.…”

Section: Contextual Advertisementmentioning

confidence: 99%

Using the geographic scopes of web documents for contextual advertising

Anastácio

Martins

Calado

2010

Proceedings of the 6th Workshop on Geographic Information Retrieval

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Geotargeting is a specialization of contextual advertising where the objective is to target ads to Website visitors concentrated in well-defined areas. Current approaches involve targeting ads based on the physical location of the visitors, estimated through their IP addresses. However, there are many situations where it would be more interesting to target ads based on the geographic scope of the target pages, i.e., on the general area implied by the locations mentioned in the textual contents of the pages. Our proposal applies techniques from the area of geographic information retrieval to the problem of geotargeting. We address the task through a pipeline of processing stages, which involves (i) determining the geographic scope of target pages, (ii) classifying target pages according to locational relevance, and (iii) retrieving ads relevant to the target page, using both textual contents and geographic scopes. Experimental results attest for the adequacy of the proposed methods in each of the individual processing stages.

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Learning to advertise

Cited by 129 publications

References 18 publications

Retrieval models for audience selection in display advertising

Retrieval models for audience selection in display advertising

A Brief Review Along With a New Proposed Approach of Data De Duplication

Using the geographic scopes of web documents for contextual advertising

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