A DECISION TREE BASED RECOMMENDATION SYSTEM FOR TOURISTS

Abstract

Tourism is a rapidly growing industry, and tourists increasingly seek personalized travel experiences. Traditional recommendation systems, such as collaborative and content-based filtering, often face challenges like cold-start problems, limited personalization, and inability to incorporate contextual factors. This paper presents a Decision Tree-Based Recommendation System for Tourists that analyzes user preferences, historical travel data, and contextual information such as budget, season, location, and activity interests. The decision tree algorithm efficiently classifies user profiles and generates personalized destination and activity recommendations. Experimental results demonstrate high accuracy and relevance, highlighting the system’s ability to enhance user satisfaction and provide interpretable, context-aware recommendations for intelligent tourism planning