research-article

How to measure information similarity in online social networks

Authors:

Danielle H. Lee,

Peter BrusilovskyAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 418, Issue C

Pages 46 - 60

https://doi.org/10.1016/j.ins.2017.07.034

Published: 01 December 2017 Publication History

Abstract

In our current knowledge-driven society, many information systems encourage users to utilize their online social connections information collections actively as useful sources. The abundant information-sharing activities among online social connections could be valuable in enhancing and developing a sophisticated user information model. In order to leverage the shared information as a user information model, our preliminary job is to determine how to measure effectively the resulting patterns. However, this task is not easy, due to multiple aspects of information and the diversity of information preferences among social connections. Which similarity measure is the most representable for the common interests of multifaceted information among online social connections? This is the main question we will explore in this paper. In order to answer this question, we considered users self-defined online social connections, specifically in Citeulike, which were built around an object-centered sociality as the gold standard of shared interests among online social connections. Then, we computed the effectiveness of various similarity measures in their capabilities to estimate shared interests. The results demonstrate that, instead of focusing on monotonous bookmark-based similarities, it is significantly better to zero in on more cognitively expressible metadata-based similarities in accounting for shared interests.

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Cited By

de Andrade RRêgo L(2023)A proposal for the EI index for fuzzy groupsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07545-327:4(2125-2137)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s00500-022-07545-3
Rohilla VChakraborty SKumar R(2022)Deep learning based feature extraction and a bidirectional hybrid optimized model for location based advertisingMultimedia Tools and Applications10.1007/s11042-022-12457-381:11(16067-16095)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s11042-022-12457-3
Tsai CBrusilovsky PPapadopoulos GSamaras GWeibelzahl SJannach DSantos O(2019)Evaluating Visual Explanations for Similarity-Based RecommendationsProceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3320435.3320465(22-30)Online publication date: 7-Jun-2019
https://dl.acm.org/doi/10.1145/3320435.3320465

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 418, Issue C

December 2017

671 pages

ISSN:0020-0255

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Copyright © Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 December 2017

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Cited By

de Andrade RRêgo L(2023)A proposal for the EI index for fuzzy groupsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07545-327:4(2125-2137)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s00500-022-07545-3
Rohilla VChakraborty SKumar R(2022)Deep learning based feature extraction and a bidirectional hybrid optimized model for location based advertisingMultimedia Tools and Applications10.1007/s11042-022-12457-381:11(16067-16095)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s11042-022-12457-3
Tsai CBrusilovsky PPapadopoulos GSamaras GWeibelzahl SJannach DSantos O(2019)Evaluating Visual Explanations for Similarity-Based RecommendationsProceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3320435.3320465(22-30)Online publication date: 7-Jun-2019
https://dl.acm.org/doi/10.1145/3320435.3320465

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