Visual and buying sequence features-based product image recommendation using optimization based deep residual network

Abstract

A recommendation system is an imaginative resolution for managing the restrictions in e-commerce services with item details and user details. Also, it is used to determine the user preferences to recommend the items they expected to buy. Several conventional collaborative filtering techniques are devised in the recommender model, but it has some complexities. Hence, an innovative optimization-driven deep residual network is devised in this paper for a product recommendation system. Here, the product of images is used for extracting features where the Convolutional neural network (CNN) features are computed, and then it is given as input to the deep residual network aimed at product recommendation. The deep residual network is trained using developed Elephant Herding Feedback Artificial Optimization (EHFAO), which is obtained by integrating Elephant Herding optimization (EHO) into the Feedback Artificial Tree (FAT). Here, the item grouping is carried out on input data based on K-means clustering. After item grouping, Cosine similarity is used to perform matching of groups, where the best group is acquired among all the available groups. Extraction of list of visitors is done from the best group. Then, the list of items is obtained from the sequence of best visitor. Next, the corresponding binary sequence is obtained for the applicable sequence of visitor. From this sequence of best visitor, the recommended product is acquired. Then, the recommended product is subjected to the sentiment analysis for which the score is determined. Here, the sentiment analysis helps to decide whether the product is recommended or not recommended. If the score is positive, then the same product is recommended; otherwise, the new product is recommended. The proposed EHFAO-based deep residual network attained better performance in comparison to the other techniques with a maximal F-measure at 84.061%, 84.061% precision, 87.845% recall along with minimal Mean Squared Error (MSE) of 0.216.