Improvising the performance of image-based recommendation system using convolution neural networks and deep learning

Abstract

Recommendation systems now hold special significance, for in a world full of choices, order is the need of the hour. Without proper sorting, the gift of choice means nothing. The online retail world is fast-paced and ever-growing. With the exponential waning of attention span, it has become crucial to convert a casual visitor into a buyer within a limited window. Different ways can be used to do this: analysing buying patterns, surveys, user–user relationships, user-item relationships, and so on. This can be done with simple data analysis or with complex algorithms—the data must be harnessed one way or another. Deep learning is a branch of machine learning that has now become synonymous with computer vision, as these deep architectures closely emulate the biological process of vision. In this paper, the primary focus is the incorporation of a recommendation system with the visual features of products. This is done with the help of a deep architecture and a series of “convolution” operations that cause the overlapping of edges and blobs in images. We find that when the dimensionality problem has been dealt with, the features extracted serve as good quality representations of the images. Our empirical study compares the different linear and nonlinear reduction techniques on convolutional neural network features for building a recommendation model entirely based on the images.