Selection and visiting sequence of daily attractions: Multi-day travel itinerary recommendation based on multi-source online data

Abstract

This article investigates the problem of recommending multi-day travel itineraries based on multi-source online data to meet the requirements of tourists who wish to plan selection and visiting sequence of daily attractions. Current research mainly uses optimization model-based methods or data-driven methods for multi-day travel itinerary recommendation. However, the former is difficult to utilize others' travel experiences and may lead to over-recommending similar category attractions, while the latter is unable to help tourists determine their specific daily itineraries. Therefore, in this study, a multi-day travel itinerary recommendation framework is proposed based on travelogue data, review data, attraction basic information from travel websites, and traffic data from online maps. The framework aims to achieve multi-day travel itinerary recommendation based on the personalized attraction preferences of tourists, while fully leveraging the travel experiences of others. Four corresponding methods are proposed for the four stages of this framework: 1) a method for mining the preferences of the target tourist for tourist attractions, 2) a method for constructing a candidate multi-day itinerary set, 3) a method for constructing a candidate single-day itinerary set, and 4) a method for recommending multi-day itineraries based on the combination of single-day itineraries. In addition, we conduct experimental studies by using real data to verify the feasibility and effectiveness of the framework. The results show that compared with existing related research, the proposed framework and methods have a significant advantage in hit rate and are helpful for tourists to determine specific daily itineraries during their travel time.