Online Ecomemerce Service Base On Recommendation System

Abstract

E-commerce websites have become increasingly popular among consumers due to their convenience. However, with the growing competition in the market, it has become essential for these websites to provide a personalized experience to their users to increase customer engagement and sales. Recommendation systems play a critical role in providing personalized recommendations to users by analyzing their behavior and purchase history. In this thesis, I propose a method for developing an e-commerce website based on a recommendation system. The solution includes using technologies such as: ReactJs, NodeJs, Express, Mongoose, Dotenv, NodeMailer, Bcrypt, Jsonwebtoken, and two algorithms are collaborative filtering models and content-based filtering. Collaborative Filtering [1]: is one of the most popular recommendation system algorithms. This algorithm is based on user ratings and similarity between users to make product recommendations. This algorithm can be implemented using user rating matrix or product interaction information. Content-Based Filtering [2]: is an algorithmic recommendation system based on product content. This algorithm uses product information such as category, description, and other attributes to make product recommendations to another users. Together, these technologies provide a powerful toolset for building an e-commerce website based on a recommendation system. They allow developers to build scalable, efficient, and secure applications that provide a personalized experience to the users. The method involves the following machine learning life cycle [3]: Data Collection: Collecting the right user data is the first stage in creating a successful recommendation system. Several sources, including past purchases, browser activity, and user reviews, may be used to collect this data. To facilitate access and analysis, collected data must be organized and maintained in a database. Data Preprocessing: Before being evaluated, data must be preprocessed after it is collected. This includes cleaning the data, removing duplicates, and formatting the data for simple analysis. Recommendation Generation: This technology provides customers with customized recommendations based on data analysis. Users can receive suggestions via email or see them posted on the website. Feedback and Improvement: The final phase entails regularly gathering user input and making necessary improvements to the proposed system. This helps ensure that the system is providing consumers with consistent and relevant recommendations.Improving user satisfaction, increasing customer engagement, and ultimately boosting sales of e-commerce websites are recommended strategies. The right recommendations have the potential to increase customer satisfaction and drive repeat business. Additionally, using modern technologies like React and NodeJS can lead to a faster and more efficient development process for an e-commerce website.