A framework for tag-aware recommender systems

Hyun Woo Kim, Hyoung Joo Kim

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.

Original languageEnglish
Pages (from-to)4000-4009
Number of pages10
JournalExpert Systems with Applications
Volume41
Issue number8
DOIs
Publication statusPublished - 2014 Jun 15
Externally publishedYes

Fingerprint

Recommender systems
Association rules

Keywords

  • Hybrid framework
  • Recommendation
  • Social tagging system
  • Tags

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

A framework for tag-aware recommender systems. / Kim, Hyun Woo; Kim, Hyoung Joo.

In: Expert Systems with Applications, Vol. 41, No. 8, 15.06.2014, p. 4000-4009.

Research output: Contribution to journalArticle

Kim, Hyun Woo ; Kim, Hyoung Joo. / A framework for tag-aware recommender systems. In: Expert Systems with Applications. 2014 ; Vol. 41, No. 8. pp. 4000-4009.
@article{4de736a6af994bf0975b17a89680e9bc,
title = "A framework for tag-aware recommender systems",
abstract = "In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.",
keywords = "Hybrid framework, Recommendation, Social tagging system, Tags",
author = "Kim, {Hyun Woo} and Kim, {Hyoung Joo}",
year = "2014",
month = "6",
day = "15",
doi = "10.1016/j.eswa.2013.12.019",
language = "English",
volume = "41",
pages = "4000--4009",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",
number = "8",

}

TY - JOUR

T1 - A framework for tag-aware recommender systems

AU - Kim, Hyun Woo

AU - Kim, Hyoung Joo

PY - 2014/6/15

Y1 - 2014/6/15

N2 - In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.

AB - In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.

KW - Hybrid framework

KW - Recommendation

KW - Social tagging system

KW - Tags

UR - http://www.scopus.com/inward/record.url?scp=84892716930&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84892716930&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2013.12.019

DO - 10.1016/j.eswa.2013.12.019

M3 - Article

AN - SCOPUS:84892716930

VL - 41

SP - 4000

EP - 4009

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 8

ER -