Batangas Travel Recommender System Using Collaborative Filtering Algorithm

Abstract

The capstone project, “Batangas Travel Recommender System using Collaborative Filtering Algorithm,” aims to create a personalized recommendation platform for travelers seeking to explore the beauty of Batangas, including its hidden destinations and local experiences. The web-based system allows users to discover tourist spots, register an account, set travel preferences, and receive tailored recommendations based on their interests and behavior. Features include a searchable list of destinations with filters, user reviews, ratings, and relevant travel information.

The primary objectives of the study were: (1) to develop a personalized recommender system using a Collaborative Filtering Algorithm, (2) to evaluate the system's quality based on ISO 9126 standards, and (3) to prepare an implementation plan. A group of 50 respondents, including Information Technology and Computer Science students and professionals, evaluated the system's performance using ISO 9126 criteria: functionality, reliability, maintainability, efficiency, usability, and portability.

The findings confirmed the successful development of the system, which significantly enhanced user experience by delivering personalized travel recommendations. Software evaluation based on ISO 9126 highlighted the system’s strengths across multiple metrics, while an implementation plan was proposed to ensure effective deployment and continuous improvement.

In conclusion, the Batangas Travel Recommender System successfully assists users in discovering tourist spots aligned with their preferences, proving the effectiveness of collaborative filtering in delivering personalized travel recommendations. The system also met high standards of functionality, reliability, and usability, ensuring its readiness for real-world application.