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.
