skip to main content
10.1145/3269206.3269312acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
short-paper

Causal Dependencies for Future Interest Prediction on Twitter

Published: 17 October 2018 Publication History

Abstract

The accurate prediction of users' future topics of interests on social networks can facilitate content recommendation and platform engagement. However, researchers have found that future interest prediction, especially on social networks such as Twitter, is quite challenging due to the rapid changes in community topics and evolution of user interactions. In this context, temporal collaborative filtering methods have already been used to perform user interest prediction, which benefit from similar user behavioral patterns over time to predict how a user's interests might evolve in the future. In this paper, we propose that instead of considering the whole user base within a collaborative filtering framework to predict user interests, it is possible to much more accurately predict such interests by only considering the behavioral patterns of the most influential users related to the user of interest. We model influence as a form of causal dependency between users. To this end, we employ the concept of Granger causality to identify causal dependencies. We show through extensive experimentation that the consideration of only one causally dependent user leads to much more accurate prediction of users' future interests in a host of measures including ranking and rating accuracy metrics.

References

[1]
Hongyun Bao, Qiudan Li, Stephen Shaoyi Liao, Shuangyong Song, and Heng Gao. 2013. A new temporal and social PMF-based method to predict users' interests in micro-blogging. Decision Support Systems 55, 3 (2013), 698--709.
[2]
Prasanta Bhattacharya and Rishabh Mehrotra. 2016. The Information Network: Exploiting Causal Dependencies in Online Information Seeking. In CHIIR'16. 223--232.
[3]
Hossein Fani, Ebrahim Bagheri, and Weichang Du. 2017. Temporally Like-minded User Community Identification through Neural Embeddings. In CIKM'17. 577-- 586.
[4]
C. W. J. Granger. 1969. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica 37, 3 (1969), 424--438.
[5]
Guibing Guo, Jie Zhang, and Neil Yorke-Smith. 2015. TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings. In AAAI'15. 123--129.
[6]
Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In RecSys'10. 135--142.
[7]
Yehuda Koren. 2009. Collaborative filtering with temporal dynamics. In SIGKDD'09. 447--456.
[8]
Ding Mingzhou, Chen Yonghong, and Bressler Steven L. 2006. Granger Causality: Basic Theory and Application to Neuroscience. Wiley-Blackwell, 437--460.
[9]
Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito, and Markus Zanker. 2012. Linked open data to support content-based recommender systems. In 8th International Conference on Semantic Systems, 2012. 1--8.
[10]
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, and How Jing. 2017. Recurrent Recommender Networks. In WSDM'17. 495--503.
[11]
Fattane Zarrinkalam, Hossein Fani, Ebrahim Bagheri, and Mohsen Kahani. 2017. Predicting Users' Future Interests on Twitter. In ECIR'17. 464--476.

Cited By

View all
  • (2024)CausalMMM: Learning Causal Structure for Marketing Mix ModelingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635766(238-246)Online publication date: 4-Mar-2024
  • (2023)A novel deep transfer learning framework with adversarial domain adaptation: application to financial time-series forecastingNeural Computing and Applications10.1007/s00521-023-09047-135:34(24037-24054)Online publication date: 4-Oct-2023
  • (2022)Exploring the Utility of Social Content for Understanding Future In-Demand SkillsProceedings of the ACM on Human-Computer Interaction10.1145/35551146:CSCW2(1-35)Online publication date: 11-Nov-2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
October 2018
2362 pages
ISBN:9781450360142
DOI:10.1145/3269206
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. granger causality
  2. twitter
  3. user interest prediction

Qualifiers

  • Short-paper

Funding Sources

  • Natural Sciences and Engineering Research Council Canada

Conference

CIKM '18
Sponsor:

Acceptance Rates

CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)14
  • Downloads (Last 6 weeks)0
Reflects downloads up to 30 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)CausalMMM: Learning Causal Structure for Marketing Mix ModelingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635766(238-246)Online publication date: 4-Mar-2024
  • (2023)A novel deep transfer learning framework with adversarial domain adaptation: application to financial time-series forecastingNeural Computing and Applications10.1007/s00521-023-09047-135:34(24037-24054)Online publication date: 4-Oct-2023
  • (2022)Exploring the Utility of Social Content for Understanding Future In-Demand SkillsProceedings of the ACM on Human-Computer Interaction10.1145/35551146:CSCW2(1-35)Online publication date: 11-Nov-2022
  • (2022)BERT and Word Embedding for Interest Mining of Instagram UsersAdvances in Computational Collective Intelligence10.1007/978-3-031-16210-7_10(123-136)Online publication date: 21-Sep-2022
  • (2021)On the Congruence Between Online Social Content and Future IT Skill DemandProceedings of the ACM on Human-Computer Interaction10.1145/34795115:CSCW2(1-27)Online publication date: 18-Oct-2021
  • (2020)Extracting User Interests from Operation Logs on Museum Devices for Post-LearningDigital Libraries at Times of Massive Societal Transition10.1007/978-3-030-64452-9_15(176-186)Online publication date: 26-Nov-2020
  • (2020)Temporal Latent Space Modeling for Community PredictionAdvances in Information Retrieval10.1007/978-3-030-45439-5_49(745-759)Online publication date: 8-Apr-2020
  • (2019)Extracting, Mining and Predicting Users' Interests from Social NetworksProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331383(1407-1408)Online publication date: 18-Jul-2019
  • (2019)Social User Interest MiningProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3332279(3235-3236)Online publication date: 25-Jul-2019

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media