Problem Statement
Planning personalized travel itineraries can be time-consuming and challenging,
considering various factors such as individual preferences, interests, and
available time. An AI-driven solution is needed to streamline and optimize the
travel planning process.
Abstract
This project aims to develop an AI-driven personalized travel itinerary planning
system. Utilizing machine learning algorithms, the system will analyze user
preferences, travel history, and real-time data to generate customized travel
itineraries, including recommendations for activities, accommodations, and
transportation.
Outcome
A user-friendly travel planning platform that leverages AI to provide personalized
recommendations, enhancing the overall travel experience for users.
Reference
In recent years, recommender systems have been used as a solution to support tourists with
recommendations oriented to maximize the entertainment value of visiting a tourist destination. However, this is not an easy task because many aspects need to be considered to make realistic recommendations: the context of a tourist destination visited, lack of updated information about points of interest, transport information, weather forecast, etc. The recommendations concerning a tourist destination must be linked to the interests and constraints of the tourist. In this research, we present a mobile recommender system based on Tourist Trip Design Problem (TTDP)/Time Depending (TD) – Orienteering Problem (OP) – Time Windows (TW), which analyzes in real time the user’s constraints and the points of interest’s constraints. For solving TTDP, we clustered preferences depending on the number of days that a tourist will visit a tourist destination using a k-means algorithm.
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