research-article

Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations

Authors:

Guangyuan Piao and

John G. BreslinAuthors Info & Claims

SEMANTiCS 2016: Proceedings of the 12th International Conference on Semantic Systems

September 2016

Pages 81 - 88

https://doi.org/10.1145/2993318.2993332

Published: 12 September 2016 Publication History

Abstract

User modeling for individual users on the Social Web plays an important role and is a fundamental step for personalization as well as recommendations. Recent studies have proposed different user modeling strategies considering various dimensions such as temporal dynamics and semantics of user interests. Although previous work proposed different user modeling strategies considering the temporal dynamics of user interests, there is a lack of comparative studies on those methods and therefore the comparative performance over each other is unknown. In terms of semantics of user interests, background knowledge from DBpedia has been explored to enrich user interest profiles so as to reveal more information about users. However, it is still unclear to what extent different types of information from DBpedia contribute to the enrichment of user interest profiles.

In this paper, we propose user modeling strategies which use Concept Frequency - Inverse Document Frequency (CF-IDF) as a weighting scheme and incorporate either or both of the dynamics and semantics of user interests. To this end, we first provide a comparative study on different user modeling strategies considering the dynamics of user interests in previous literature to present their comparative performance. In addition, we investigate different types of information (i.e., categories, classes and connected entities via various properties) for entities from DBpedia and the combination of them for extending user interest profiles. Finally, we build our user modeling strategies incorporating either or both of the best-performing methods in each dimension. Results show that our strategies outperform two baseline strategies significantly in the context of link recommendations on Twitter.

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https://doi.org/10.3390/su16020781
Zarrinkalam FNoughabi HNoorian ZFani HBagheri E(2024)Predicting users’ future interests on social networks: A reference frameworkInformation Processing & Management10.1016/j.ipm.2024.10376561:5(103765)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103765
Guesmi MChatti MKadhim LJoarder SAin Q(2023)Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence EncodersMultimodal Technologies and Interaction10.3390/mti70900917:9(91)Online publication date: 15-Sep-2023
https://doi.org/10.3390/mti7090091
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Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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cover image ACM Other conferences

SEMANTiCS 2016: Proceedings of the 12th International Conference on Semantic Systems

September 2016

207 pages

ISBN:9781450347525

DOI:10.1145/2993318

Editors:
Anna Fensel
STI Innsbruck, University of Innsbruck, Austria
,
Amrapali Zaveri
Stanford University, USA
,
Sebastian Hellmann
AKSW/KILT, Institute for Applied Informatics (InfAI), Leipzig, Germany
,
Tassilo Pellegrini
University of Applied Sciences St. Poelten, Austria

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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Ghent University: Ghent University
AIT: Austrian Institute of Technology
Stanford University: Stanford University
Wolters Kluwer: Wolters Kluwer, Germany
Semantic Web Company: Semantic Web Company

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Association for Computing Machinery

New York, NY, United States

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Published: 12 September 2016

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Science Foundation Ireland

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SEMANTiCS 2016

SEMANTiCS 2016: 12th International Conference on Semantic Systems

September 12 - 15, 2016

Leipzig, Germany

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SEMANTiCS 2016 Paper Acceptance Rate 18 of 85 submissions, 21%;

Overall Acceptance Rate 40 of 182 submissions, 22%

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Cited By

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https://doi.org/10.3390/su16020781
Zarrinkalam FNoughabi HNoorian ZFani HBagheri E(2024)Predicting users’ future interests on social networks: A reference frameworkInformation Processing & Management10.1016/j.ipm.2024.10376561:5(103765)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103765
Guesmi MChatti MKadhim LJoarder SAin Q(2023)Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence EncodersMultimodal Technologies and Interaction10.3390/mti70900917:9(91)Online publication date: 15-Sep-2023
https://doi.org/10.3390/mti7090091
Joshi MBhuvana J(2023)Personalized Recommendation after Classification of Tweets to Predict Depression using Sentiment Analysis2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)10.1109/ICAICCIT60255.2023.10465832(827-832)Online publication date: 23-Nov-2023
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Ezaldeen HBisoy SMisra RAlatrash R(2023)Semantics aware intelligent framework for content-based e-learning recommendationNatural Language Processing Journal10.1016/j.nlp.2023.1000083(100008)Online publication date: Jun-2023
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Natarajan SVairavasundaram SKotecha KIndragandhi VPalani SSaini JRavi L(2022)CD-SemMF: Cross-Domain Semantic Relatedness Based Matrix Factorization Model Enabled With Linked Open Data for User Cold Start IssueIEEE Access10.1109/ACCESS.2022.317556610(52955-52970)Online publication date: 2022
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Hayama T(2021)Analyzing Features of Passive Twitter’s Users to Estimate Passive Twitter-User’s InterestsIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3493979(476-481)Online publication date: 14-Dec-2021
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