Towards a Personalized Item Recommendation Approach in Social Tagging Systems Using Intuitionistic Fuzzy DBSCAN

Chun Guan, Kevin Kam Fung Yuen*, Yong Yue

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

In folksonomies, users annotate items with abundant personalized tags. The tags can be used in recommendation systems to produce meaningful information. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can be applied in recommendation systems. This paper proposes the Intuitionistic Fuzzy DBSCAN (IF-DBSCAN) for the personalized item recommendation in folksonomy. The IF-DBSCAN is used to cluster items with respect to the user-defined tags. The Intuitionistic Fuzzy Set (IFS) is used to represent tag values which are vague and uncertain. DBSCAN can cluster items with the tags represented by using IFSs into different groups. An example of movie recommendation is demonstrated for the applicability of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages361-364
Number of pages4
ISBN (Electronic)9781538658369
DOIs
Publication statusPublished - 9 Nov 2018
Event10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018 - Hangzhou, China
Duration: 25 Aug 201826 Aug 2018

Publication series

NameProceedings - 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018
Volume1

Conference

Conference10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018
Country/TerritoryChina
CityHangzhou
Period25/08/1826/08/18

Keywords

  • DBSCAN
  • Folksonomy
  • Intuitionistic Fuzzy Set
  • Movie recommendation
  • Social tagging system

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