Expert detection and recommendation model with user-generated tags in collaborative tagging systems

Mengmeng Shen, Jun Wang, Ou Liu, Haiying Wang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

Original languageEnglish
Pages (from-to)24-45
Number of pages22
JournalJournal of Database Management
Volume31
Issue number4
DOIs
Publication statusPublished - 1 Oct 2020
Externally publishedYes

Keywords

  • Collaborative Tagging System
  • Community Detection
  • Community Expert Detection
  • Community Expert Recommendation
  • Tags

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