Machine Learning Based Predicting for Consumer Purchasing Recommendations in Social Commerce Networks

Social commerce networks increased rapidly during recent years because internet purchasing merged with social media. These networks have transformed the complete process through which people find items and evaluate them alongside their purchase decisions. The process of forecasting consumer buying behavior across social networks proves difficult because it requires handling substantial amounts of information about social contacts and individual interests together with environmental factors. The investigation describes a thorough machine learning procedure to forecast social commerce network consumer purchasing behavior. The prediction of human behaviors and tastes relies on multiple machine learning strategies such as deep learning together with collaborative filtering along with content-based filtering. The accuracy of purchase predictions increases through evaluation of three key social influence elements across user content along with user relationships and peer reviews. A pair of distinctive features stand out as the main strengths and advantages of our innovative blend model implementation.