A content based and collaborative filtering recommender system

Vignesh Thannimalai, Li Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This research proposes a new recommendation system for recommendation generation based on users' ratings and personal profiles. Motivated by existing studies, firstly we propose item-based collaborative filtering to recommend tourist spots based on users' rating. In addition, we incorporate the content-based filtering algorithm with Naïve Bayes Classifier, for recommendation generation. Detailed analysis of these proposed methods are discussed which will give a clear view on how the core part of the recommendation systems has been implemented. The proposed TRS was evaluated using several data sets to indicate its efficiency.

Original languageEnglish
Title of host publicationProceedings of 2021 International Conference on Machine Learning and Cybernetics, ICMLC 2021
PublisherIEEE
ISBN (Electronic)9781665466080
DOIs
Publication statusPublished - 2021
Event20th International Conference on Machine Learning and Cybernetics, ICMLC 2021 - Adelaide, United States
Duration: 4 Dec 20215 Dec 2021

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2021-December
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference20th International Conference on Machine Learning and Cybernetics, ICMLC 2021
Country/TerritoryUnited States
CityAdelaide
Period4/12/215/12/21

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