To Analyze the Various Machine Learning Algorithms That Can Effectively Process Large Volumes of Data and Extract Relevant Information for Personalized Travel Recommendations

Recommendation systems are evolving predominantly across tourism, e-commerce, entertainment, and health industries. They filter information by analyzing heterogeneous network data and offering users pertinent suggestions. When choosing tourist places and hotels worldwide, many visitors and travelers...

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Bibliographic Details
Published in:SN computer science 2024-04, Vol.5 (4), p.336, Article 336
Main Authors: Karthiyayini, J., Anandhi, R. J.
Format: Article
Language:English
Subjects:
Accuracy
Advances in Computational Approaches for Image Processing
Algorithms
Artificial intelligence
Cloud Applications and Network Security
Collaboration
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data acquisition
Data processing
Data Structures and Information Theory
Datasets
Feature extraction
Information Systems and Communication Service
Literature reviews
Machine learning
Neural networks
Pattern Recognition and Graphics
R&D
Recommender systems
Research & development
Sentiment analysis
Software Engineering/Programming and Operating Systems
Survey Article
Tourism
Tourist attractions
Travel
Trend analysis
Vision
Wireless Networks
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Summary:Recommendation systems are evolving predominantly across tourism, e-commerce, entertainment, and health industries. They filter information by analyzing heterogeneous network data and offering users pertinent suggestions. When choosing tourist places and hotels worldwide, many visitors and travelers frequently rely on places of interest, numerical ratings, and text reviews. This paper focuses on tourism recommendations and comprehensively reviews the field. The review highlights the types of recommendation systems, Artificial intelligence (AI), and Machine Learning (ML) in recommendation systems and the data acquisition, data processing, and feature extraction for Tourism Recommendation Systems (TRS). The development, trend analysis, theoretical foundation, and algorithmic methodologies utilized to develop recommendation systems are also covered in this work. The simulation platforms, datasets, and performance measures used to analyze TRS are reviewed and outlined. Finally, this study summarizes the status of research in the TRS and highlights the challenges, research gaps, and future directions in developing a proficient tourism recommendation system.
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-024-02667-x