An intelligent deep learning based product image recommendation and classification system

Recently, recommendation system (RS) has gained significant attention in several industries and business sectors. At the same time, image recommendation is also found helpful to determine the relevant objects that exist in the form of image. It is mainly based on the extraction of different features and utilize it to get recommended outcomes. The recently developed deep learning (DL) models can be applied to design effective product image recommendation and classification systems. In this aspect, this paper designs an intelligent deep learning enabled product image recommendation and classification (IDL-PIRC) system. The proposed IDL-PIRC technique aims to examine the input image to recommend and classify products based on the user query. In addition, the proposed IDL-PIRC technique involves Gaussian filtering (GF) based pre-processing to eradicate the existence of noise exist in it. Moreover, the fusion-based feature extraction technique uses the Grey-Level Run Length Matrix (GLRLM) and Residual Network (ResNet152) models. Furthermore, the kNN based ranking approach is employed for product recommendation and cascaded neural network (CNN) is utilized for product classification. A wide range of simulations take place and the results are inspected interms of different evaluation parameters