The TagRec framework as a toolkit for the development of tag-based recommender systems

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

Abstract

Recommender systems have become important tools to support users in identifying relevant content in an overloaded information space. To ease the development of recommender systems, a number of recommender frameworks have been proposed that serve a wide range of application domains. Our TagRec framework is one of the few examples of an open-source framework tailored towards developing and evaluating tag-based recommender systems. In this paper, we present the current, updated state of TagRec, and we summarize and re.ect on four use cases that have been implemented with TagRec: (i) tag recommendations, (ii) resource recommendations, (iii) recommendation evaluation, and (iv) hashtag recommendations. To date, TagRec served the development and/or evaluation process of tag-based recommender systems in two large scale European research projects, which have been described in 17 research papers. .us, we believe that this work is of interest for both researchers and practitioners of tag-based recommender systems.

Original languageEnglish
Title of host publicationUMAP 2017 - Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization
PublisherAssociation of Computing Machinery
Pages23-28
Number of pages6
ISBN (Electronic)9781450350679
DOIs
Publication statusPublished - 9 Jul 2017
Event25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017 - Bratislava, Slovakia
Duration: 9 Jul 201712 Jul 2017

Conference

Conference25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017
CountrySlovakia
CityBratislava
Period9/07/1712/07/17

Keywords

  • Hashtag recommendation
  • Recommendation evaluation
  • Recommender framework
  • Recommender systems
  • Tag recommendation

ASJC Scopus subject areas

  • Software

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