Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems |
| Seiten | 59-66 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 9 Sept. 2012 |
| Veranstaltung | 6th ACM Conference on Recommender Systems, RecSys 2012 - Dublin, Irland Dauer: 9 Sept. 2012 → 13 Sept. 2012 |
Publikationsreihe
| Name | RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems |
|---|
Abstract
The SocialWeb is successfully established, and steadily growing in terms of users, content and services. People generate and consume data in real-time within social networking services, such as Twitter, and increasingly rely upon continuous streams of messages for real-time access to fresh knowledge about current affairs. In this paper, we focus on analyzing social streams in real-time for personalized topic recommen- dation and discovery. We consider collaborative filtering as an online ranking problem and present Stream Ranking Matrix Factorization { RMFX {, which uses a pairwise approach to matrix factorization in order to optimize the personalized ranking of topics. Our novel approach follows a selective sampling strategy to perform online model updates based on active learning principles, that closely simulates the task of identifying relevant items from a pool of mostly uninteresting ones. RMFX is particularly suitable for large scale applications and experiments on the 476 million Twitter tweets dataset show that our online approach largely outperforms recommendations based on Twitter's global trend, and it is also able to deliver highly competitive Top-N recommendations faster while using less space than Weighted Regularized Matrix Factorization (WRMF), a state-of-the-art matrix factorization technique for Collaborative Filtering, demonstrating the efficacy of our approach.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems. 2012. S. 59-66 (RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Real-time top-N recommendation in social streams
AU - Diaz-Aviles, Ernesto
AU - Drumond, Lucas
AU - Schmidt-Thieme, Lars
AU - Nejdl, Wolfgang
PY - 2012/9/9
Y1 - 2012/9/9
N2 - The SocialWeb is successfully established, and steadily growing in terms of users, content and services. People generate and consume data in real-time within social networking services, such as Twitter, and increasingly rely upon continuous streams of messages for real-time access to fresh knowledge about current affairs. In this paper, we focus on analyzing social streams in real-time for personalized topic recommen- dation and discovery. We consider collaborative filtering as an online ranking problem and present Stream Ranking Matrix Factorization { RMFX {, which uses a pairwise approach to matrix factorization in order to optimize the personalized ranking of topics. Our novel approach follows a selective sampling strategy to perform online model updates based on active learning principles, that closely simulates the task of identifying relevant items from a pool of mostly uninteresting ones. RMFX is particularly suitable for large scale applications and experiments on the 476 million Twitter tweets dataset show that our online approach largely outperforms recommendations based on Twitter's global trend, and it is also able to deliver highly competitive Top-N recommendations faster while using less space than Weighted Regularized Matrix Factorization (WRMF), a state-of-the-art matrix factorization technique for Collaborative Filtering, demonstrating the efficacy of our approach.
AB - The SocialWeb is successfully established, and steadily growing in terms of users, content and services. People generate and consume data in real-time within social networking services, such as Twitter, and increasingly rely upon continuous streams of messages for real-time access to fresh knowledge about current affairs. In this paper, we focus on analyzing social streams in real-time for personalized topic recommen- dation and discovery. We consider collaborative filtering as an online ranking problem and present Stream Ranking Matrix Factorization { RMFX {, which uses a pairwise approach to matrix factorization in order to optimize the personalized ranking of topics. Our novel approach follows a selective sampling strategy to perform online model updates based on active learning principles, that closely simulates the task of identifying relevant items from a pool of mostly uninteresting ones. RMFX is particularly suitable for large scale applications and experiments on the 476 million Twitter tweets dataset show that our online approach largely outperforms recommendations based on Twitter's global trend, and it is also able to deliver highly competitive Top-N recommendations faster while using less space than Weighted Regularized Matrix Factorization (WRMF), a state-of-the-art matrix factorization technique for Collaborative Filtering, demonstrating the efficacy of our approach.
KW - Collaborative filtering
KW - Matrix factorization
KW - Online learning
KW - Ranking
KW - Selective sampling
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84867345632&partnerID=8YFLogxK
U2 - 10.1145/2365952.2365968
DO - 10.1145/2365952.2365968
M3 - Conference contribution
AN - SCOPUS:84867345632
SN - 9781450312707
T3 - RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems
SP - 59
EP - 66
BT - RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems
T2 - 6th ACM Conference on Recommender Systems, RecSys 2012
Y2 - 9 September 2012 through 13 September 2012
ER -