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IJSTR >> Volume 5 - Issue 4, April 2016 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Using Deep Learning Neural Networks To Find Best Performing Audience Segments

[Full Text]

 

AUTHOR(S)

Anup Badhe

 

KEYWORDS

deep learning neural networks, mobile advertising yield optimization, increase ROI on ad dollars, smart ad serving

 

ABSTRACT

Finding the appropriate mobile audience for mobile advertising is always challenging since there are many data points that need to be considered and assimilated before a target segment can be created and used in ad serving by any ad server. Deep learning neural networks have been used in machine learning to use multiple processing layers to interpret large datasets with multiple dimensions to come up with a high-level characterization of the data. During a request for an advertisement and subsequently serving of the advertisement on the mobile device there are many trackers that are fired collecting a lot of data points. If the user likes the advertisement and clicks on it, another set of trackers give additional information resulting from the click. This information is aggregated by the ad server and shown in its reporting console. The same information can form the basis of machine learning by feeding this information to a deep learning neural network to come up with audiences that can be targeted based on the product that is advertised.

 

REFERENCES

[1] http://www.faqs.org/faqs/ai-faq/neural-nets/part2/.

[2] https://visualstudiomagazine.com/articles/2014/01/01/how-to-standardize-data-for-neural-networks.aspx

[3] http://neuralnetworksanddeeplearning.com/chap1.html