Research on Tourism Route Recommendation Strategy Based on Convolutional Neural Network and Collaborative Filtering Algorithm

With improving people’s living standards, tourism has become essential leisure and entertainment. At present, it has begun to shift from a quantity-oriented tourism method to a quality-oriented tourism method. It is difficult for passengers to choose the route that suits them from the numerous route...

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Bibliographic Details
Published in:Security and communication networks Vol. 2022; pp. 1 - 9
Main Author: He, Shan
Format: Journal Article
Language:English
Published: London Hindawi 07.05.2022
John Wiley & Sons, Inc
Subjects:
Algorithms
Artificial neural networks
Collaboration
Customers
Decomposition
Feature extraction
Filtration
Information overload
Neural networks
Ratings & rankings
Recommender systems
Social networks
Tourism
Travel
ISSN:1939-0114, 1939-0122
Online Access:Get full text
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Summary:With improving people’s living standards, tourism has become essential leisure and entertainment. At present, it has begun to shift from a quantity-oriented tourism method to a quality-oriented tourism method. It is difficult for passengers to choose the route that suits them from the numerous routes. Given the above problems, this study proposes a travel route recommendation algorithm that combines a convolutional neural network and collaborative filtering. The algorithm uses a convolutional neural network to extract the latent features in the customer and travel itinerary data and then uses the matrix factorization method based on collaborative filtering to perform score prediction. The experimental results show that the algorithm can meet the travel requirements of different customers. At the same time, the recommendation accuracy of the tourist route is improved, and technology and method are provided for realizing the personalized recommendation service of the tourist route.
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ISSN:1939-0114
1939-0122
DOI:10.1155/2022/4659567