demonstration

Event Recommendation using Twitter Activity

Authors:

Axel Magnuson,

Vijay Dialani,

Deepa MallelaAuthors Info & Claims

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Pages 331 - 332

https://doi.org/10.1145/2792838.2796556

Published: 16 September 2015 Publication History

Get Access

Abstract

User interactions with Twitter (social network) frequently take place on mobile devices - a user base that it strongly caters to. As much of Twitter's traffic comes with geo-tagging information associated with it, it is a natural platform for geographic recommendations. This paper proposes an event recommender system for Twitter users, which identifies twitter activity co-located with previous events, and uses it to drive geographic recommendations via item-based collaborative filtering.

References

[1]

Hila Becker, Mor Naaman, and Luis Gravano. Beyond trending topics: Real-world event identification on twitter. ICWSM, 11:438--441, 2011.

Google Scholar

[2]

Michael D. Ekstrand, Michael Ludwig, Joseph A. Konstan, and John T. Riedl. Rethinking the recommender research ecosystem: Reproducibility, openness, and lenskit. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys '11, pages 133--140, New York, NY, USA, 2011. ACM.

Digital Library

Google Scholar

[3]

Ryong Lee and Kazutoshi Sumiya. Measuring geographical regularities of crowd behaviors for twitter-based geo-social event detection. In Proceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks, pages 1--10. ACM, 2010.

Digital Library

Google Scholar

[4]

Daniel Ramage, David Hall, Ramesh Nallapati, and Christopher D Manning. Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1, pages 248--256. Association for Computational Linguistics, 2009.

Digital Library

Google Scholar

[5]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web, WWW '01, pages 285--295, New York, NY, USA, 2001. ACM.

Digital Library

Google Scholar

[6]

Qiankun Zhao, Prasenjit Mitra, and Bi Chen. Temporal and information flow based event detection from social text streams. In AAAI, volume 7, pages 1501--1506, 2007.

Digital Library

Google Scholar

Cited By

View all

Fawzi MMagdy W(2024)"Pinocchio had a Nose, You have a Network!": On Characterizing Fake News Spreaders on Arabic Social MediaProceedings of the ACM on Human-Computer Interaction10.1145/36537028:CSCW1(1-20)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653702
Jelodar HWang YXiao GRabbani MZhao RAyobi SHu PMasood I(2020)Recommendation system based on semantic scholar mining and topic modeling on conference publicationsSoft Computing10.1007/s00500-020-05397-3Online publication date: 3-Nov-2020
https://doi.org/10.1007/s00500-020-05397-3
Rizi FGranitzer MHung CPapadopoulos G(2019)Predicting event attendance exploring social influenceProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297622(2131-2134)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297622
Show More Cited By

Index Terms

Event Recommendation using Twitter Activity
1. Information systems

Recommendations

Personalized News Recommendation Using Twitter
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03

Online news reading has become a popular way to read news articles from a huge collection of news sources around the globe. News recommender systems help users manage this flood by suggesting articles based on user interests rather than presenting ...
Activity Recommendation with Partners

Recommending social activities, such as watching movies or having dinner, is a common function found in social networks or e-commerce sites. Besides certain websites which manage activity-related locations (e.g., foursquare.com), many items on product ...
Personalized Tourist Package Recommendation using Graph Based Approach
UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

Location Based Social Networks (LBSN) benefit the users by allowing them to share their locations and life moments with their friends. The users can also review the locations they have visited. Classical recommender systems provide users a ranked list ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

September 2015

414 pages

ISBN:9781450336925

DOI:10.1145/2792838

General Chairs:
Hannes Werthner
TU Wien, Austria
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria
,
Program Chairs:
Jennifer Golbeck
University of Maryland, USA
,
Giovanni Semeraro
University of Bari Aldo Moro, Italy

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2015

Check for updates

Author Tags

Qualifiers

Demonstration

Funding Sources

Idaho Department of Commerce
National Science Foundation

Conference

RecSys '15

Sponsor:

SIGCHI

RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

Acceptance Rates

RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
437
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Fawzi MMagdy W(2024)"Pinocchio had a Nose, You have a Network!": On Characterizing Fake News Spreaders on Arabic Social MediaProceedings of the ACM on Human-Computer Interaction10.1145/36537028:CSCW1(1-20)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653702
Jelodar HWang YXiao GRabbani MZhao RAyobi SHu PMasood I(2020)Recommendation system based on semantic scholar mining and topic modeling on conference publicationsSoft Computing10.1007/s00500-020-05397-3Online publication date: 3-Nov-2020
https://doi.org/10.1007/s00500-020-05397-3
Rizi FGranitzer MHung CPapadopoulos G(2019)Predicting event attendance exploring social influenceProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297622(2131-2134)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297622
Magdy AAbdelhafeez LKang YOng EMokbel M(2019)Microblogs data management: a surveyThe VLDB Journal10.1007/s00778-019-00569-6Online publication date: 18-Sep-2019
https://doi.org/10.1007/s00778-019-00569-6

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Personalized News Recommendation Using Twitter

Activity Recommendation with Partners

Personalized Tourist Package Recommendation using Graph Based Approach

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations