Abstract
In recent years, there has been a growing interest in travel applications that provide on-site personalized tourist spot recommendations. While generally helpful, most available options offer choices based solely on static information on places of interest without consideration of such dynamic factors as weather, time of day, and congestion, and with a focus on helping the tourist decide what single spot to visit next. Such limitations may prevent visitors from optimizing the use of their limited resources (i.e., time and money). Some existing studies allow users to calculate a semi-optimal tour visiting multiple spots in advance, but their on-site use is difficult due to the large computation time, no consideration of dynamic factors, etc. To deal with this situation, we formulate a tour score approach with three components: static tourist information on the next spot to visit, dynamic tourist information on the next spot to visit, and an aggregate measure of satisfaction associated with visiting the next spot and the set of subsequent spots to be visited. Determining the tour route that produces the best overall tour score is an NP-hard problem for which we propose three algorithms variations based on the greedy method. To validate the usefulness of the proposed approach, we applied the three algorithms to 20 points of interest in Higashiyama, Kyoto, Japan, and confirmed that the output solution was superior to the model route for Kyoto, with computation times of the three algorithms of 1.9±0.1, 2.0±0.1, and 27.0±1.8 s.
Highlights
In recent years, demand in the tourism industry has continued to increase, as has the cost of trips taken by tourists [1]
Recognizing that tourist plans are often disrupted by unexpected events such as sudden heavy rain, congestion, special events, and temporary closures, leading to visitor disappointment and dissatisfaction, we propose a tourism planning approach that takes into account such unexpected events, as well as a number of additional dynamic factors, seeking to optimize the visitor’s overall tourist experience
Since this paper focuses on the construction of a tourism recommendation algorithm, we present the problem of existing tourism recommendation algorithms and the position of our study
Summary
Demand in the tourism industry has continued to increase, as has the cost of trips taken by tourists [1]. Recognizing that tourist plans are often disrupted by unexpected events such as sudden heavy rain, congestion, special events, and temporary closures, leading to visitor disappointment and dissatisfaction, we propose a tourism planning approach that takes into account such unexpected events, as well as a number of additional dynamic factors, seeking to optimize the visitor’s overall tourist experience. A visitor’s level of satisfaction with a particular spot may differ depending on the time of day (e.g., a spot with a beautiful night view), making it important to recommend the most satisfying time of day for the spot in order to improve the overall satisfaction of tourists [11] Some existing systems such as P-Tour [12] allow users to calculate a semi-optimal tour visiting multiple spots before starting sightseeing, but their on-site use is difficult due to the large computation time, no consideration of dynamic factors, etc. The rest of the paper is organized as follows: Section 2 reviews related work; Section 3 defines the problem; Section 4 presents our proposed on-site planning algorithms; Section 5 describes the evaluation experiment; Sections 6 and 7 describe and discuss experimental results; Section 8 provides a summary and conclusions
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