Recommendation System based on Consumer Buying Behavior using Deep Learning Techniques

Abstract

"Understanding customer purchasing behavior is of utmost importance in today's market. Accurate analysis of customer purchasing behavior enables the development of effective marketing strategies, leading to product growth and increased profits. The growing prevalence of E-commerce platforms and the increasing number of online shoppers have led to the popularity of recommender systems. These systems play a significant role in assisting consumers in making informed purchase decisions by utilizing data and insights from other users. These systems play a critical role in enhancing user satisfaction and loyalty, ultimately resulting in long-term benefits. Recommender systems have a fundamental objective of providing users with proactive and valuable item recommendations, effectively saving their valuable time and effort. To accomplish this, recommender systems employ various techniques, including predicting user ratings for individual items and organizing them in descending order based on these predictions. They utilize diverse information sources, such as item content, user behavior history, and demographic data, to generate relevant recommendations. Despite facing challenges such as scalability, cold-start issues, and data sparsity, recommender systems persist in delivering accurate suggestions to users."