Topic-graph based recommendation on social tagging systems: A study on researchgate

Yuyun Chen, Hang Dong, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social Tagging Systems (STSs), allowing users to annotate online resources with freely chosen key words, are an essential type of application in Web 2.0. Recommendation in STSs can prevent information overload and support users to locate relevant items for interaction. This article applies a Topic-Graph Based Recommendation approach. First, we discover semantics behind tags through topic inferencing with Latent Dirichlet Allocation (LDA). Second, we conduct Graph-Based Recommendation for tags and users. The approach is applied on a real-word representative data sample collected from the Academic Social Networking Site ResearchGate. The widely used Co-occurrence Based Graph Recommendation is implemented as a baseline approach. Our preliminary human evaluation shows that the Topic-Graph Based Recommendation can complement to the Co-occurrence baseline to provide more reliable results. Future studies are provided on leveraging future features and information for recommendation from researcher-generated social media data on a large scale.

Original languageEnglish
Title of host publicationProceedings of the 2018 International Conference on Data Science and Information Technology, DSIT 2018
PublisherACM Association for Computing Machinery
Pages138-143
Number of pages6
ISBN (Electronic)9781450365215
DOIs
Publication statusPublished - 20 Jul 2018
Event2018 International Conference on Data Science and Information Technology, DSIT 2018 - Singapore, Singapore
Duration: 20 Jul 201822 Jul 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2018 International Conference on Data Science and Information Technology, DSIT 2018
Country/TerritorySingapore
CitySingapore
Period20/07/1822/07/18

Keywords

  • Academic Social Networking Sites
  • Data mining
  • Graph-based recommendation
  • Probabilistic Topic Models
  • Social Tagging Systems

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