Webpage Depth Viewability Prediction using Deep Sequential Neural Networks

ABSTRACT:

Show publicizing is the most vital income hotspot for distributers in the web based distributing industry. The advertisement valuing principles are moving to another model in which promotions are paid just in the event that they are seen. Subsequently, a vital issue for distributers is to foresee the likelihood that a promotion at a given page profundity will be appeared on a client’s screen for a specific abide time.

This paper proposes profound learning models dependent on Long Short-Term Memory (LSTM) to anticipate the perceptibility of any page profundity for some random stay time. The principle curiosity of our best model comprises in the blend of bi-directional LSTM systems, encoder-decoder structure, and remaining associations. The test results over a dataset gathered from a substantial online distributer show that the proposed LSTM-based consecutive neural systems beat the correlation techniques as far as expectation execution.

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