Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems |
| Seiten | 229-232 |
| Seitenumfang | 4 |
| 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
Recommender systems make product suggestions that are tailored to the user's individual needs and represent powerful means to combat information overload. In this paper, we focus on the item prediction task of Recommender Systems and present SwarmRankCF, a method to automatically optimize the performance quality of recommender systems using a Swarm Intelligence perspective. Our approach, which is well-founded in a Particle Swarm Optimization framework, learns a ranking function by optimizing the combination of unique characteristics (i.e., features) of users, items and their interactions. In particular, we build feature vectors from a factorization of the user-item interaction matrix, and directly optimize Mean Average Precision metric in order to learn a linear ranking model for personalized recommendations. Our experimental evaluation, on a real world online radio dataset, indicates that our approach is able to find ranking functions that significantly improve the performance of the system for the Top-N recommendation task.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
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- BibTex
- RIS
RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems. 2012. S. 229-232 (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 - Swarming to Rank for Recommender Systems
AU - Diaz-Aviles, Ernesto
AU - Georgescu, Mihai
AU - Nejdl, Wolfgang
PY - 2012/9/9
Y1 - 2012/9/9
N2 - Recommender systems make product suggestions that are tailored to the user's individual needs and represent powerful means to combat information overload. In this paper, we focus on the item prediction task of Recommender Systems and present SwarmRankCF, a method to automatically optimize the performance quality of recommender systems using a Swarm Intelligence perspective. Our approach, which is well-founded in a Particle Swarm Optimization framework, learns a ranking function by optimizing the combination of unique characteristics (i.e., features) of users, items and their interactions. In particular, we build feature vectors from a factorization of the user-item interaction matrix, and directly optimize Mean Average Precision metric in order to learn a linear ranking model for personalized recommendations. Our experimental evaluation, on a real world online radio dataset, indicates that our approach is able to find ranking functions that significantly improve the performance of the system for the Top-N recommendation task.
AB - Recommender systems make product suggestions that are tailored to the user's individual needs and represent powerful means to combat information overload. In this paper, we focus on the item prediction task of Recommender Systems and present SwarmRankCF, a method to automatically optimize the performance quality of recommender systems using a Swarm Intelligence perspective. Our approach, which is well-founded in a Particle Swarm Optimization framework, learns a ranking function by optimizing the combination of unique characteristics (i.e., features) of users, items and their interactions. In particular, we build feature vectors from a factorization of the user-item interaction matrix, and directly optimize Mean Average Precision metric in order to learn a linear ranking model for personalized recommendations. Our experimental evaluation, on a real world online radio dataset, indicates that our approach is able to find ranking functions that significantly improve the performance of the system for the Top-N recommendation task.
KW - Collaborative filtering
KW - Matrix factorization
KW - PSO
KW - Recommender systems
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=84867366723&partnerID=8YFLogxK
U2 - 10.1145/2365952.2366001
DO - 10.1145/2365952.2366001
M3 - Conference contribution
AN - SCOPUS:84867366723
SN - 9781450312707
T3 - RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems
SP - 229
EP - 232
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 -