A personalized multimodal tourist tour planner

Gavalas, Damianos; Kasapakis, Vlasios; Konstantopoulos, Charalampos; Pantziou, Grammati; Vathis, Nikolaos; Zaroliagis, Christos

doi:10.1145/2677972.2677977

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…(2) The popularity of POI The popularity of POI is calculated by combining the number of photos in the historical travelogues shared by visitors and the cooccurrence information of attractions by weighting, as in Equation (6).…”

Section: Construct the Poi Transfer Graphmentioning

confidence: 99%

“…Based on the problems encountered by current users in travel planning, tourism route planning came into being. To get a high-quality solution in travel planning, we need to consider many factors and establish corresponding evaluation models according to different standards [5,6]. For example, Rahimi and Xin further extended the existing work by studying the periodicity of space and time in user checkin data and proposed two new recommendation algorithms [7].…”

Section: Introductionmentioning

confidence: 99%

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Personalized Travel Recommendation Based on the Fusion of TGI and POI Algorithms

Lü¹,

Zhang

2022

Wireless Communications and Mobile Computing

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On the way to travel, the public expect to get a tourism experience with low cost, convenient travel, and high comfort. At the same time, they also have different tourism needs such as history and culture, natural landscape, and food shopping. To address the problem that traditional travel route recommendation algorithms have limited accuracy and only analyze text or pictures alone, we propose a personalized travel route recommendation algorithm that integrates text and photo information from travelogues and obtain the historical tourism footprint of tourists by analyzing travel notes. According to the frequency and cooccurrence of scenic spots in the travel notes and the number of photos taken by each scenic spot, the popularity of scenic spots and the interest preferences of various types of tourists are analyzed. Under the given starting and ending points or passing points, the optimal tourism route generation method is designed. Experiments on the real data set of Ctrip Travel website show that the recommendation accuracy of this algorithm is significantly improved compared with the traditional algorithm which only uses travel notes text or photos. Compared with the algorithm that only considers the popularity of interest points or tourists’ interest preferences, the accuracy of the route recommended by the algorithm is improved. Compared with the algorithm that only considers the cooccurrence of scenic spots or only considers the influence of photos, this algorithm can obtain a better popularity score of scenic spots. This method integrates the two kinds of information including picture and text, fully considering the interest of users with high practicability.

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Section: Construct the Poi Transfer Graphmentioning

confidence: 99%