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short-paper

The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems

Authors:

Dominik Kowald,

Elisabeth LexAuthors Info & Claims

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media

Pages 237 - 242

https://doi.org/10.1145/2914586.2914617

Published: 10 July 2016 Publication History

Abstract

In this paper, we study factors that influence tag reuse behavior in social tagging systems. Our work is guided by the activation equation of the cognitive model ACT-R, which states that the usefulness of information in human memory depends on the three factors usage frequency, recency and semantic context. It is our aim to shed light on the influence of these factors on tag reuse. In our experiments, we utilize six datasets from the social tagging systems Flickr, CiteULike, BibSonomy, Delicious, LastFM and MovieLens, covering a range of various tagging settings. Our results confirm that frequency, recency and semantic context positively influence the reuse probability of tags. However, the extent to which each factor individually influences tag reuse strongly depends on the type of folksonomy present in a social tagging system. Our work can serve as guideline for researchers and developers of tag-based recommender systems when designing algorithms for social tagging environments.

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Cited By

Schedl MMoscati MSguerra BHennequin RLex EAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Psychology-informed Information Access Systems WorkshopProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635722(1216-1217)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635722
Liu Y(2024)Understanding Media Effects Through Media Technological Attributes: Young People’s Second Screening Use and Political EfficacySouthern Communication Journal10.1080/1041794X.2024.235652289:3-4(212-227)Online publication date: 26-May-2024
https://doi.org/10.1080/1041794X.2024.2356522
Kowald DReiter-Haas MKopeinik SSchedl MLex E(2024)Transparent Music Preference Modeling and Recommendation with a Model of Human Memory TheoryA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_4(113-136)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_4
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Index Terms

The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
      1. Social tagging systems
    2. Collaborative and social computing theory, concepts and paradigms
      1. Social tagging
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media

July 2016

354 pages

ISBN:9781450342476

DOI:10.1145/2914586

General Chairs:
Jamie Blustein
Dalhousie University, Canada
,
Eelco Herder
L3S Research Center, Germany
,
Program Chairs:
Jessica Rubart
OWL University of Applied Sciences, Germany
,
Helen Ashman
University of South Australia

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 10 July 2016

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Short-paper

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HT '16

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SIGWEB

HT '16: 27th ACM Conference on Hypertext and Social Media

July 10 - 13, 2016

Nova Scotia, Halifax, Canada

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HT '16 Paper Acceptance Rate 16 of 54 submissions, 30%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Schedl MMoscati MSguerra BHennequin RLex EAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Psychology-informed Information Access Systems WorkshopProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635722(1216-1217)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635722
Liu Y(2024)Understanding Media Effects Through Media Technological Attributes: Young People’s Second Screening Use and Political EfficacySouthern Communication Journal10.1080/1041794X.2024.235652289:3-4(212-227)Online publication date: 26-May-2024
https://doi.org/10.1080/1041794X.2024.2356522
Kowald DReiter-Haas MKopeinik SSchedl MLex E(2024)Transparent Music Preference Modeling and Recommendation with a Model of Human Memory TheoryA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_4(113-136)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_4
Pawłowska JRydzewska KWierzbicki A(2023)Using Cognitive Models to Understand and Counteract the Effect of Self-Induced Bias on Recommendation AlgorithmsJournal of Artificial Intelligence and Soft Computing Research10.2478/jaiscr-2023-000813:2(73-94)Online publication date: 11-Mar-2023
https://doi.org/10.2478/jaiscr-2023-0008
Schedl MGómez ELex EChua TLauw HSi LTerzi ETsaparas P(2023)Trustworthy Algorithmic Ranking SystemsProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3572723(1240-1243)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3572723
Lex ESchedl M(2022)Psychology-informed Recommender Systems TutorialProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547375(714-717)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3547375
Lex ESchedl MElsweiler DKruschwitz ULudwig B(2022)Psychology-informed Recommender Systems: A Human-Centric Perspective on Recommender SystemsProceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505841(367-368)Online publication date: 14-Mar-2022
https://dl.acm.org/doi/10.1145/3498366.3505841
Schedl MGómez ELex EAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Retrieval and Recommendation Systems at the Crossroads of Artificial Intelligence, Ethics, and RegulationProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532683(3420-3424)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532683
Kowald DLacic E(2022)Popularity Bias in Collaborative Filtering-Based Multimedia Recommender SystemsAdvances in Bias and Fairness in Information Retrieval10.1007/978-3-031-09316-6_1(1-11)Online publication date: 19-Jun-2022
https://doi.org/10.1007/978-3-031-09316-6_1
Reiter-Haas MParada-Cabaleiro ESchedl MMotamedi ETkalcic MLex E(2021)Predicting Music Relistening Behavior Using the ACT-R FrameworkProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3478846(702-707)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3460231.3478846
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