Product recommendation is vital for improving the experience of users and boosting sales. Many conventional product recommender systems depend on the website browsing history of customers for precisely predicting the preferences of online users for product recommendations. Various existing methods have been developed for examining the recommendation of a product to the user, but they are not effective. Thus, this work devises a new technique, named Stacked Convolutional Neural Network with Secretary Gannet Bird Optimization Algorithm (SCNN_SGBOA). Here, the input taken from E-commerce data is demonstrated in three representations, like Context-aware model, Purchase Behavior, and User rating model. In the user rating model, the user review of the product based on a review rating is considered. In the purchase behavior model, the product’s user review based on purchasing behavior is regraded, and the context-aware model considers the user review for the product in terms of the day and time at which the product is bought. Next, all three models are combined to obtain a feature vector. Following this, context-aware personalized recommendation is executed when a new user is encountered by employing SCNN, where SCNN is tuned using the SGBOA approach. Furthermore, the newly presented SCNN_SGBOA technique measured a maximum precision of 91.877%, a recall of 93.877%, an F-measure of 92.866%, an accuracy of 91.754%, a Hit ratio of 0.977, a Normalized Discounted Cumulative Gain (NDCG) of 0.958, and coverage of 0.965.
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