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A New Information-Theoretic Method for Advertisement Conversion Rate Prediction for Large-Scale Sparse Data Based on Deep Learning

1
School of Computer Science and Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2
Beijing Insititute of Control Engineering, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(6), 643; https://doi.org/10.3390/e22060643
Received: 8 April 2020 / Revised: 31 May 2020 / Accepted: 8 June 2020 / Published: 10 June 2020
With the development of online advertising technology, the accurate targeted advertising based on user preferences is obviously more suitable both for the market and users. The amount of conversion can be properly increased by predicting the user’s purchasing intention based on the advertising Conversion Rate (CVR). According to the high-dimensional and sparse characteristics of the historical behavior sequences, this paper proposes a LSLM_LSTM model, which is for the advertising CVR prediction based on large-scale sparse data. This model aims at minimizing the loss, utilizing the Adaptive Moment Estimation (Adam) optimization algorithm to mine the nonlinear patterns hidden in the data automatically. Through the experimental comparison with a variety of typical CVR prediction models, it is found that the proposed LSLM_LSTM model can utilize the time series characteristics of user behavior sequences more effectively, as well as mine the potential relationship hidden in the features, which brings higher accuracy and trains faster compared to those with consideration of only low or high order features. View Full-Text
Keywords: information-theoretic method; advertising conversion rate; LSTM; time series; deep learning; online advertising information-theoretic method; advertising conversion rate; LSTM; time series; deep learning; online advertising
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Xia, Q.; Lv, J.; Ma, S.; Gao, B.; Wang, Z. A New Information-Theoretic Method for Advertisement Conversion Rate Prediction for Large-Scale Sparse Data Based on Deep Learning. Entropy 2020, 22, 643.

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