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2022 | OriginalPaper | Buchkapitel

Collaborative Travel Recommender System Based on Malayalam Travel Reviews

verfasst von : V. K. Muneer, K. P. Mohamed Basheer

Erschienen in: Artificial Intelligence and Speech Technology

Verlag: Springer International Publishing

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Abstract

Recommender System is an unsupervised machine learning technique used for users to make decisions on their own choices and interests. Travel and Tourism is one of the important domains in this area. Rapid growth of redundant, heterogeneous data and web has created the importance for Recommender system. Travel Recommender System helps to users or travelers to decide on their choices and preferences on their travel. The work proposes a machine learning approach for a personalized travel recommender system in Malayalam Language, one of the prominent languages used in Southern part of India. The system developed using Collaborative Filtering technique, Malayalam Text processing with the help of Cosine similarity and TF-IDF methods. Data has been taken from the largest Malayalam Travel group in Facebook named “Sanchari”. A customized scraping algorithm also developed to collect relevant and enough information from social media. The RS could suggest most suitable destinations to each of users with the accuracy of 93%, which is fair result as compared with other algorithms on same domain.

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Vorheriges Kapitel Reinforcing Digital Forensics Through Intelligent Behavioural Evidence Analysis: Social Media Hate Speech Profiling

Nächstes Kapitel Hybrid Framework Model for Group Recommendation

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Titel: Collaborative Travel Recommender System Based on Malayalam Travel Reviews
verfasst von: V. K. Muneer
K. P. Mohamed Basheer
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Speech Technology
Print ISBN: 978-3-030-95710-0

Electronic ISBN: 978-3-030-95711-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-95711-7_53

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