skip to main content
10.1145/2433396.2433470acmconferencesArticle/Chapter ViewAbstractPublication PageswsdmConference Proceedingsconference-collections
research-article

Retweet or not?: personalized tweet re-ranking

Published: 04 February 2013 Publication History

Abstract

With Twitter being widely used around the world, users are facing enormous new tweets every day. Tweets are ranked in chronological order regardless of their potential interestedness. Users have to scan through pages of tweets to find useful information. Thus more personalized ranking scheme is needed to filter the overwhelmed information. Since retweet history reveals users' personal preference for tweets, we study how to learn a predictive model to rank the tweets according to their probability of being retweeted. In this way, users can find interesting tweets in a short time. To model the retweet behavior, we build a graph made up of three types of nodes: users, publishers and tweets. To incorporate all sources of information like users' profile, tweet quality, interaction history, etc, nodes and edges are represented by feature vectors. All these feature vectors are mapped to node weights and edge weights. Based on the graph, we propose a feature-aware factorization model to re-rank the tweets, which unifies the linear discriminative model and the low-rank factorization model seamlessly. Finally, we conducted extensive experiments on a real dataset crawled from Twitter. Experimental results show the effectiveness of our model.

References

[1]
D. Agarwal and B.-C. Chen. Regression-based latent factor models. In KDD, pages 19--28, 2009.
[2]
D. Boyd, S. Golder, and G. Lotan. Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In HICSS, pages 1--10, 2010.
[3]
L. Hong, O. Dan, and B. D. Davison. Predicting popular messages in twitter. In WWW (Companion Volume), pages 57--58, 2011.
[4]
Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD, pages 426--434, 2008.
[5]
H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In WSDM, pages 287--296, 2011.
[6]
S. A. Macskassy and M. Michelson. Why do people retweet? anti-homophily wins the day! In ICWSM, 2011.
[7]
S. Petrovic, M. Osborne, and V. Lavrenko. Rt to win! predicting message propagation in twitter. In ICWSM, 2011.
[8]
D. Ramage, S. T. Dumais, and D. J. Liebling. Characterizing microblogs with topic models. In ICWSM, 2010.
[9]
S. Rendle. Factorization machines with libfm. ACM TIST, 3(3):57, 2012.
[10]
S. Rendle. Learning recommender systems with adaptive regularization. In WSDM, 2012.
[11]
D. M. Romero, W. Galuba, S. Asur, and B. A. Huberman. Influence and passivity in social media. In ECML/PKDD (3), pages 18--33, 2011.
[12]
B. Suh, L. Hong, P. Pirolli, and E. H. Chi. Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In SocialCom/PASSAT, pages 177--184, 2010.
[13]
I. Uysal and W. B. Croft. User oriented tweet ranking: a filtering approach to microblogs. In CIKM, 2011.
[14]
J. Yang and S. Counts. Predicting the speed, scale, and range of information diffusion in twitter. In ICWSM, 2010.

Cited By

View all
  • (2023)Serendipitous User Recommendation in Twitter by Consider Unexpected and Useful InterestsTwitterにおける興味の意外性と有用性を考慮したセレンディピティなユーザの推薦Joho Chishiki Gakkaishi10.2964/jsik_2023_02733:3(267-288)Online publication date: 30-Sep-2023
  • (2023)Modelling Delayed Redemption with Importance Sampling and Pre-Redemption EngagementProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599867(3926-3936)Online publication date: 6-Aug-2023
  • (2023)Tweet recommendation using Clustered Bert and Word2vec Models2023 International Conference on Smart Applications, Communications and Networking (SmartNets)10.1109/SmartNets58706.2023.10215867(i-vi)Online publication date: 25-Jul-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
WSDM '13: Proceedings of the sixth ACM international conference on Web search and data mining
February 2013
816 pages
ISBN:9781450318693
DOI:10.1145/2433396
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: 04 February 2013

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. recommender system
  2. social media

Qualifiers

  • Research-article

Conference

WSDM 2013

Acceptance Rates

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)24
  • Downloads (Last 6 weeks)1
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Serendipitous User Recommendation in Twitter by Consider Unexpected and Useful InterestsTwitterにおける興味の意外性と有用性を考慮したセレンディピティなユーザの推薦Joho Chishiki Gakkaishi10.2964/jsik_2023_02733:3(267-288)Online publication date: 30-Sep-2023
  • (2023)Modelling Delayed Redemption with Importance Sampling and Pre-Redemption EngagementProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599867(3926-3936)Online publication date: 6-Aug-2023
  • (2023)Tweet recommendation using Clustered Bert and Word2vec Models2023 International Conference on Smart Applications, Communications and Networking (SmartNets)10.1109/SmartNets58706.2023.10215867(i-vi)Online publication date: 25-Jul-2023
  • (2022)The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social MediaApplied Sciences10.3390/app1204215712:4(2157)Online publication date: 18-Feb-2022
  • (2022)Sentiment Text Analysis for Providing Individualized Feed of Recommendations Using Reinforcement Learning2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892012(1-7)Online publication date: 18-Jul-2022
  • (2022)Directional user similarity model for personalized recommendation in online social networksJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.10.01734:10(10205-10216)Online publication date: Nov-2022
  • (2021)Social Media and Microblogs Credibility: Identification, Theory Driven Framework, and RecommendationIEEE Access10.1109/ACCESS.2021.31144179(137744-137781)Online publication date: 2021
  • (2020)Social Network Analysis as a Tool for Data Analysis and Visualization in Information Behaviour and Interactive Information Retrieval ResearchProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3378018(477-480)Online publication date: 14-Mar-2020
  • (2020)Mining User Interests from Social MediaProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412167(3519-3520)Online publication date: 19-Oct-2020
  • (2020)Discerning Influence Patterns with Beta-Poisson Factorization in Microblogging EnvironmentsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289793232:6(1092-1103)Online publication date: 1-Jun-2020
  • Show More Cited By

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