Development of Hybrid Personalized E-commerce Using Collaborative Filtering and Content-Based Filtering for South Cartel Clothing Company
Jcyle Anne T. Balmadres
a
,
Kristine Bartolome
a
,
Roi Gerome B. Bunyi
a
,
Jeffrey Rafael B. Jacobo
a
,
Jay-ar P. Lalata
a
,
Ace C. Lagman
a
,
Ma. Corazon G. Fernando
a
a Information Technology Department, FEU Institute of Technology, Sampaloc, Metro Manila, Manila, Philippines
Abstract: E-commerce plays an essential role in selling products or services online because it can reach more customers than traditional retail. If the customer data is appropriately mishandled, it disrupts the business’ data organization and poor customer relationship management. The study focuses on creating an e-commerce website that efficiently handles the data and integrates a personalized hybrid recommender system. Content-based and collaborative filtering methods were used in the recommender system to improve customer relationship management, streamline procedures, organize inventory and sales, and increase profits. Sales forecasting using ARIMA was also added to use the customer data for efficient business decisions. ISO 9126 was the software quality model used to evaluate the developed system using the software quality characteristics functionality, usability, maintainability, and efficiency. The system got an overall mean score of 4.57, which is excellent, which means the system can perform smooth transactions from ordering up to the checkout and organized products, sales, and inventory. The integration of the recommender systems was able to give recommendations based on the customer's preferences, which enhances the user experience that may lead to an increase in sales of the business since the suggestions are tailored recommendations to the users.