Survey on Recommender Systems for Market Analysis using Deep Learning

Abstract

Deep learning and machine learning techniques in marketing analysis have gained tremendous popularity because of its” learning feature.” These techniques are applied in various ways within business organizations specially in marketing to handle tasks such as prediction, feature extraction, natural language processing and recommendation etc. In the domain of recommender system relationship between items will create denser representations. For improved and successful recommendations, embeddings (continuous vector representations) are created to reduce categorical variables. Business intelligence in marketing analysis is about understanding structure and growth of market for estimating beneficial policies for cost minimization and maximization profit based on the customer data. The consumer behavioral data is shattered in different silos, which makes data processing and analysis difficult. This study aims to provide comprehensive review of deep learning-based methodologies for recommendation task along with embedding techniques to create composite embedding from domain specific partial embeddings of customer data for market analysis which is shattered in silos. The study explains about graph convolution networks and knowledge graphs for learning disentangled embeddings to improve recommendation. The study reviews deep learning-based methods, algorithms, its applications and provide new perspective strategies in the area of recommender systems. The results and discussion section summarizes the trends of deep learning-based methods for recommender systems for market analysis and highlights open issues to improve recommendations.