Our Research “Personalized Recommendation Systems for E-Commerce on Social
Media Platforms using Big Data Analytics “is a as e-commerce offers more and more
choices for users, its structure becomes more and more complicated. Inevitably, it brings
about the problem of information overload. The solution to this problem is an e-commerce
personalized recommendation system using machine learning technology. People often
seem confused when facing extensive information and cannot grasp the key points. This
paper studies the personalized recommendation technology of e-commerce: deeply
analyzes the related technologies and algorithms of the e-commerce recommendation
system and proposes the latest architecture of the e-commerce recommendation system
according to the current development status of the e-commerce recommendation system.
The system recommends accuracy and real-time requirements and divides the system
into two parts: offline mining and online recommendation and analyzes and implements
the functions and technologies of each part. User-based recommender systems,
collaborative filtering recommender systems, and content-based recommender systems
are analyzed, respectively. The personalized recommendation cannot only quickly help
customers find the required commodity information in a wide range of complex
information but also can compare more commodity information to help customers to
judge. However, the existing recommendation system has some problems such as the
lack of recommendation personality, the reduced relevance of recommendation, and the
poor timeliness of recommendation. Finally, a recommendation system that combines
three recommendation algorithms is designed, and experiments are carried out. The
newly designed recommendation system is compared with three different
recommendation systems, and a summary and outlook are made. Based on the
introduction of the relevant theories, characteristics, and mainstream technologies of
personalized recommendation based on machine learning, this document presents a
constructive example of a model based on the factors that influence personalized ecommerce
information recommendations in the retail sector. Through questionnaire
surveys, we analyze and design the influencing factors for consumers to purchase
personalized products after the survey and build a project using state-of-the-art field
learning techniques. Through the model to test the eight hypotheses proposed in this
paper, the results show that customer income level, customer online shopping
experience, commodity prices, product quality, recommendation relevance, credit
evaluation, and service quality will have a significant positive impact on shopping
willingness and ultimately affect the customer’s shopping behavior. E-commerce platform
can use this influencing factor to establish personalized information recommendation
service mode.