Abstract
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.
Highlights
With the rapid development of the Internet economy, online advertising has become the main advertising channel
In response to the problems above, this paper proposes a hybrid model based on the deep neural network, Large Scale Linear Model and Long Short-Term Memory Networks (LSLM_LSTM), according to the user behavior sequence data, product information, and advertisement information of the e-commerce platform, in order to estimate the Conversion Rate (CVR) of advertisements more efficiently and accurately
Gradient Boosting Decision Tree (GBDT) and long short-term memory network (LSTM) is better than Factorization Machine (FM) in the experiment
Summary
With the rapid development of the Internet economy, online advertising has become the main advertising channel. In response to the problems above, this paper proposes a hybrid model based on the deep neural network, Large Scale Linear Model and Long Short-Term Memory Networks (LSLM_LSTM), according to the user behavior sequence data, product information, and advertisement information of the e-commerce platform, in order to estimate the CVR of advertisements more efficiently and accurately. This model supports the prediction with both high-order and low-order features.
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