Details
| Original language | English |
|---|---|
| Title of host publication | RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems |
| Pages | 309-312 |
| Number of pages | 4 |
| Publication status | Published - 26 Sept 2010 |
| Event | 4th ACM Recommender Systems Conference, RecSys 2010 - Barcelona, Spain Duration: 26 Sept 2010 → 30 Sept 2010 |
Publication series
| Name | RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems |
|---|
Abstract
In this paper, we propose a method for automatic tagging sparse and short textual resources. In the presence of a new resource, our method creates an ad hoc corpus of related resources, then applies Latent Dirichlet Allocation (LDA) to elicit latent topics for the resource and the associated corpus. This is done in order to automatically tag the resource based on the most likely tags derived from the latent topics identified. We evaluate our method, using an offline analysis on publicly available BibSonomy dataset and an online study, showing its effectiveness.
Keywords
- Automatic annotation, LDA, Recommender systems, Social tagging, Web 2.0
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Software
- Engineering(all)
- Control and Systems Engineering
Cite this
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RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems. 2010. p. 309-312 (RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - LDA for On-the-Fly Auto Tagging
AU - Diaz-Aviles, Ernesto
AU - Georgescu, Mihai
AU - Stewart, Avaré
AU - Nejdl, Wolfgang
PY - 2010/9/26
Y1 - 2010/9/26
N2 - In this paper, we propose a method for automatic tagging sparse and short textual resources. In the presence of a new resource, our method creates an ad hoc corpus of related resources, then applies Latent Dirichlet Allocation (LDA) to elicit latent topics for the resource and the associated corpus. This is done in order to automatically tag the resource based on the most likely tags derived from the latent topics identified. We evaluate our method, using an offline analysis on publicly available BibSonomy dataset and an online study, showing its effectiveness.
AB - In this paper, we propose a method for automatic tagging sparse and short textual resources. In the presence of a new resource, our method creates an ad hoc corpus of related resources, then applies Latent Dirichlet Allocation (LDA) to elicit latent topics for the resource and the associated corpus. This is done in order to automatically tag the resource based on the most likely tags derived from the latent topics identified. We evaluate our method, using an offline analysis on publicly available BibSonomy dataset and an online study, showing its effectiveness.
KW - Automatic annotation
KW - LDA
KW - Recommender systems
KW - Social tagging
KW - Web 2.0
UR - http://www.scopus.com/inward/record.url?scp=78649942742&partnerID=8YFLogxK
U2 - 10.1145/1864708.1864774
DO - 10.1145/1864708.1864774
M3 - Conference contribution
AN - SCOPUS:78649942742
SN - 9781450304429
T3 - RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems
SP - 309
EP - 312
BT - RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems
T2 - 4th ACM Recommender Systems Conference, RecSys 2010
Y2 - 26 September 2010 through 30 September 2010
ER -