research-article

Investigation of Egocentric Social Structures for Diversity-Enhancing Followee Recommendations

Authors:

Erjon Skenderi,

Ekaterina Olshannikova,

Jukka Huhtamäki,

Heikki HuttunenAuthors Info & Claims

UMAP'19 Adjunct: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization

Pages 257 - 261

https://doi.org/10.1145/3314183.3323460

Published: 06 June 2019 Publication History

Abstract

The increasing amount of data in social media enables new advanced user modeling approaches. This paper focuses on user profiling for diversity-enhancing recommender systems for finding new followees on Twitter. By combining social network analysis with Latent Dirichlet Allocation based content analysis, we defined three egocentric structural positions on the network extracted from Twitter data: Mentions of Mentions, Community Cluster, Dormant Ties (and the rest as a baseline condition). In addition to describing the data analysis procedure, we report preliminary empirical findings on a user-centered evaluation study of recommendations based on the proposed matching strategy and the presented structural positions. The investigation of the possible overlaps of the groups and the participants' evaluations of perceived relevance of the recommendation imply that the three positions are sufficiently mutually exclusive and thus could serve as new diversity-enhancing mechanisms in various people recommender systems.

References

[1]

Fabian Abel, Qi Gao, Geert-Jan Houben, and Ke Tao. 2011. Analyzing user modeling on twitter for personalized news recommendations. In UMAP'2011. User Modeling, Adaption and Personalization. Springer, Berlin, Heidelberg, 1--12.

Digital Library

[2]

Kazuhiro Akiyama, Tadahiko Kumamoto, and Akiyo Nadamoto. 2017. Emotionbased method for latent followee recommendation in Twitter. In Proc. of the 19th International Conference on Information Integration and Web-based Applications & Services. ACM, Salzburg, Austria, 121--125.

Digital Library

[3]

Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008, 10 (oct 2008), 1742--5468.

[4]

Giuliana Carullo, Aniello Castiglione, Alfredo De Santis, and Francesco Palmieri. 2015. A triadic closure and homophily-based recommendation system for online social networks. World Wide Web 18, 6 (2015), 1579--1601.

Digital Library

[5]

Christian Fuchs. 2017. Social media: A critical introduction. Sage, London, UK.

Digital Library

[6]

Rogg Garcia-Gavilanes and Xavier Amatriain. 2010. Weighted content based methods for recommending connections in online social networks. In Procedings of the ACM Conference on Recommender systems. ACM, New York, 68--71.

[7]

Davide Feltoni Gurini, Fabio Gasparetti, Alessandro Micarelli, and Giuseppe Sansonetti. 2013. A Sentiment-Based Approach to Twitter User Recommendation. In Proceedings of the 5th ACM RecSys Workshop on Recommender Systems and the Social Web. ACM, New York, 1--4.

[8]

John Hannon, Kevin McCarthy, and Barry Smyth. 2011. Finding useful users on twitter: twittomender the followee recommender. In Proc. of the European Conference on Information Retrieval. Springer, Dublin, Ireland, 784--787.

Digital Library

[9]

Natali Helberger, Kari Karppinen, and Lucia D'Acunto. 2018. Exposure diversity as a design principle for recommender systems. Information, Communication & Society 21, 2 (2018), 191--207.

[10]

Gueorgi Kossinets and Duncan JWatts. 2009. Origins of homophily in an evolving social network. American journal of sociology 115, 2 (2009), 405--450.

[11]

Matev? Kunaver and Toma? Po?rl. 2017. Diversity in recommender systems--A survey. Knowledge-Based Systems 123 (2017), 154--162.

Digital Library

[12]

Su Mon Kywe, Ee-Peng Lim, and Feida Zhu. 2012. A survey of recommender systems in twitter. In Proc. of the International Conference on Social Informatics. Springer, Washington, DC, USA, 420--433.

Digital Library

[13]

Stefano Mizzaro, Marco Pavan, and Ivan Scagnetto. 2015. Content-based similarity of twitter users. In European Conference on Information Retrieval. Springer, Cham, 507--512.

[14]

Ekaterina Olshannikova, Thomas Olsson, Jukka Huhtamäki, and Hannu Kärkkäinen. 2017. Conceptualizing big social data. Journal of Big Data 4, 1 (2017), 3.

[15]

Bansari Shah, Ananda Prakash Verma, and Shailendra Tiwari. 2018. User Interest Modeling from Social Media Network Graph, Enriched with Semantic Web. In Proceedings of International Conference on Computational Intelligence and Data Engineering. Springer, Singapore, 55--64.

[16]

Marc A Smith, Itai Himelboim, Lee Rainie, and Ben Shneiderman. 2015. The structures of Twitter crowds and conversations. In Transparency in social media. Springer, Cham, 67--108.

[17]

Loren Terveen and David W McDonald. 2005. Social matching: A framework and research agenda. ACM transactions on computer-human interaction (TOCHI) 12, 3 (2005), 401--434.

Digital Library

[18]

Chun-Hua Tsai and Peter Brusilovsky. 2018. Beyond the Ranked List: User-Driven Exploration and Diversification of Social Recommendation. In Proceedings of the 2018 Conference on Human Information Interaction&Retrieval - IUI '18. ACM, New York, 239--250.

Digital Library

[19]

Jianshu Weng, Ee-Peng Lim, Jing Jiang, and Qi He. 2010. Twitterrank: finding topic-sensitive influential twitterers. In Proc. of the third ACM international conference on Web search and data mining. ACM, New-York, NY, USA, 261--270.

Digital Library

[20]

Quan Yuan, Gao Cong, Kaiqi Zhao, Zongyang Ma, and Aixin Sun. 2015. Who, where, when, and what: A nonparametric bayesian approach to context-aware recommendation and search for twitter users. ACM Transactions on Information Systems (TOIS) 33, 1 (2015), 2.

Digital Library

Cited By

Skenderi EHuhtamäki JLaaksonen SStefanidis K(2024)INCEPT: A Framework for Duplicate Posts Classification with Combined Text RepresentationsACM Transactions on the Web10.1145/367732218:3(1-24)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.1145/3677322
Schelenz L(2022)Diversity Concepts in Computer Science and Technology Development: A CritiqueScience, Technology, & Human Values10.1177/0162243922112254948:5(1054-1079)Online publication date: 13-Sep-2022
https://doi.org/10.1177/01622439221122549
Olshannikova ESkenderi EOlsson TKoivunen SHuhtamäki J(2022)Utilizing Structural Network Positions to Diversify People Recommendations on TwitterAdvances in Human-Computer Interaction10.1155/2022/65843942022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6584394

Index Terms

Investigation of Egocentric Social Structures for Diversity-Enhancing Followee Recommendations
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
      1. Social network analysis
    2. Collaborative and social computing theory, concepts and paradigms
      1. Social networks
      2. Social recommendation

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Information & Contributors

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Published In

cover image ACM Conferences

UMAP'19 Adjunct: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization

June 2019

455 pages

ISBN:9781450367110

DOI:10.1145/3314183

General Chairs:
George Angelos Papadopoulos
University of Cyprus, Cyprus
,
George Samaras
University of Cyprus, Cyprus
,
Stephan Weibelzahl
PFH Private University of Applied Sciences Göttingen, Germany
,
Program Chairs:
Dietmar Jannach
Alpen-Adria-Universität Klagenfurt, Austria
,
Olga C. Santos
aDeNu Research Group - UNED, Spain

Copyright © 2019 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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Published: 06 June 2019

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Business Finland project Big Match (3166/31/2017 and 3074/31/2017). We thank all the members of t

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UMAP '19

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UMAP '19: 27th Conference on User Modeling, Adaptation and Personalization

June 9 - 12, 2019

Larnaca, Cyprus

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UMAP'19 Adjunct Paper Acceptance Rate 30 of 122 submissions, 25%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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UMAP '25

Sponsor:
sigchi
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33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

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Cited By

Skenderi EHuhtamäki JLaaksonen SStefanidis K(2024)INCEPT: A Framework for Duplicate Posts Classification with Combined Text RepresentationsACM Transactions on the Web10.1145/367732218:3(1-24)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.1145/3677322
Schelenz L(2022)Diversity Concepts in Computer Science and Technology Development: A CritiqueScience, Technology, & Human Values10.1177/0162243922112254948:5(1054-1079)Online publication date: 13-Sep-2022
https://doi.org/10.1177/01622439221122549
Olshannikova ESkenderi EOlsson TKoivunen SHuhtamäki J(2022)Utilizing Structural Network Positions to Diversify People Recommendations on TwitterAdvances in Human-Computer Interaction10.1155/2022/65843942022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6584394

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