An efficient attention-based hybridized deep learning network with deep RBM features for customer behavior prediction in digital marketing

Abstract

Purpose An efficient customer behavior prediction model is designed using deep learning techniques. The necessary data used for the implementation are taken from standard datasets and presented to perform subsequent tasks. Here, deep restricted Boltzmann machines (RBM) features are retrieved from the input images. Further, the extracted deep RBM features are presented to the customer behavior prediction phase. Here, the attention-based hybrid deep learning (A-HDL) technique is designed based on the incorporation of a dilated deep temporal convolutional network (dilated-DTCN) and a weighted recurrent neural network (weighted RNN). Moreover, the weights in RNN are tuned using a modernized random parameter-based cheetah optimizer (MRPCO). Further, various experiments were performed on the implemented framework, and it secured an enhanced customer behavior prediction rate than the conventional models. Design/methodology/approach A novel hybrid deep network-based customer behavior prediction model was developed to predict the behavior of the customer so the companies yield more income by advertising their products based on the predicted results. Findings When considering the first dataset, the designed customer behavior prediction mechanism produced 94% accuracy, which is higher than the conventional techniques such as long short-term memory (LSTM), DTCN, RNN and A-HDL with 88%, 87%, 89% and 93%. Originality/value The precision and the accuracy of the developed MRPCO-A-HDL-based customer behavior prediction model progressed than the conventional techniques and algorithms.

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