INTELLIGENT VEHICLE RECOMMENDATION SYSTEM BASED ON BUDGET & USER PREFERENCES

This paper presents a recommendation system designed to suggest suitable cars and bikes based on the user's budget and personal preferences The system will first remove vehicles that exceed the specified budget or fail to meet essential requirements. It will then rank the remaining vehicles using a weighted scoring method based on factors such as mileage, fuel type, safety, and intended usage. The recommendations will include vehicle specifications, simple explanations, and feature comparisons. The literature review examined content-based recommendation, constraint-based filtering, and multi-criteria decision-making methods that support the proposed approach. It also highlighted the need to combine budget filtering, weighted scoring, explanations, and vehicle comparisons in a single system while assessing recommendation performance and software quality. An evaluation plan has been prepared to compare the proposed method with a basic budget-based approach. The evaluation will consider budget compliance, response time, user feedback, and ranking metrics. Software quality will also be assessed using static code analysis, Lines of Code (LOC), and Cyclomatic Complexity. Since the application has not yet been developed or tested, experimental results and code measurement tables are still pending. The actual performance of the proposed method will be determined after implementation and evaluation.

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