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Abstract

E-commerce has gradually become one of the most important driving forces for the sustained and vigorous development of country's consumer market. As a result of the rapid development of network and informatization, the application and maturity of technologies like the Internet, terminal equipment, logistics, and payment, and the continuous improvement of people's consumption concepts is growing. The typical marketing methodology does not allow organizations to completely comprehend the needs of their customers. Selling products to customers is the only concern of the e-commerce platforms.They can only concentrate on the product because they don't know what the customer wants.As a result of these limitations, a model for precision marketing that employs integrated convolution neural networks with Bi LSTM (Bidirectional Long Short Term Memory) to create detailed portraits of the target customers. Users' customized items and services are becoming increasingly more dependent on personalized suggestion services as the ecommerce business expands globally. They have the potential to lower the price users pay to gather information while also increasing sales for the company. The use of user reviews to address classic product recommender system research issues has become increasingly common in recent years. Using a convolution and bidirectional long short-term memory hybrid neural network, the review semantics extractor learns how to represent product reviews. Product Recommendations Generator models a user's preference for items based on their prior interactions. In this research a Predictive Integrated Convolution Neural Network with Bi LSTM (PICNN-BiLSTM) is proposed for predictive marketing of e-commerce applications. The proposed model considers Amazon dataset for market prediction and the results represent that the proposed model performance is higher.