Abstract
In recent years, “free travel” has been increasingly popular. How to plan personalized travel routes based on the perspective of tourists, rather than that of tourism intermediaries, is in great need. However, some factors reflecting tourists’ preferences are ignored in the related work. What’s more, the evaluation about scenic spots is incomplete. Besides, real data sets are seldom used in existing works. We propose a novel route-planning method that considerate multiple factors (that is, the distance between sites, initial travel position, initial departure time, time duration of tour, total cost, scores and popularities of sites) comprehensively, and routes were rated by what we call a comprehensive attractiveness index. We conducted comprehensive case studies based on the real-world data of sites from the Baidu and Xiecheng websites and found that our proposed method is feasible. It is also found that the genetic algorithm outperformed two baseline ones in terms of run time.
Highlights
With the continuous maturity of cloud computing technology and intelligent terminal technology, the personalized demands of users will be greatly satisfied
In order to fully utilize hidden features, this paper proposes a new matrix factorization (MF) model with deep features learning, which integrates a convolutional neural network (CNN) [6]
Our work considered some other important factors that are closely related to the real situation, including the number of pictures of each site on the Xiecheng website’s reviews, the Baidu index, and the ticket prices of the sites
Summary
With the continuous maturity of cloud computing technology and intelligent terminal technology, the personalized demands of users will be greatly satisfied. People can get a real-time travel route through mobile terminals, such as cell phone or Pad. In recent years, ‘‘free travel’’ tourism mode has been increasingly popular. How to plan personalized travel routes based on the perspective of tourists (rather than the perspective of tourism intermediaries) remains to be studied. There are several problems in the related research, which makes them difficult to satisfy the personalized tourism. The related researches mainly consider the types of scenic spots, travel costs and the distance between scenic spots. Some other factors reflecting tourists’ preferences are ignored, such as the starting time, the starting place and travel time.
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