Abstract
Collaborative tagging sites allow users to save and annotate their favorite Web contents with tags. These tags provide a novel source of information for collaborative filtering. However, major gap exists in how to integrate tagging information into traditional recommender systems for better recommendation quality, due to the difficulty to quantize these semantic tags. This paper proposes a novel approach to convert this semantic information into quantitative values from a smoothing point of view, taking advantage of the topicbased method, and then make recommendation in a traditional user-based CF fashion based on the smoothed user-item matrix. Experiments on two real-world collaborative tagging datasets prove the effectiveness of our approach.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010 |
| Pages | 452-456 |
| Number of pages | 5 |
| DOIs | |
| State | Published - 2010 |
| Event | 2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010 - QingDao, China Duration: Jul 15 2010 → Jul 17 2010 |
Other
| Other | 2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010 |
|---|---|
| Country | China |
| City | QingDao |
| Period | 7/15/10 → 7/17/10 |
Fingerprint
Keywords
- Collaborative filtering
- Recommender systems
- Smoothing
- Tag
- Weighing
ASJC Scopus subject areas
- Information Systems and Management
- Management Science and Operations Research
Cite this
Tag-based smoothing for item recommendation. / Peng, Jing; Zeng, Dajun.
Proceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010. 2010. p. 452-456 5551603.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Tag-based smoothing for item recommendation
AU - Peng, Jing
AU - Zeng, Dajun
PY - 2010
Y1 - 2010
N2 - Collaborative tagging sites allow users to save and annotate their favorite Web contents with tags. These tags provide a novel source of information for collaborative filtering. However, major gap exists in how to integrate tagging information into traditional recommender systems for better recommendation quality, due to the difficulty to quantize these semantic tags. This paper proposes a novel approach to convert this semantic information into quantitative values from a smoothing point of view, taking advantage of the topicbased method, and then make recommendation in a traditional user-based CF fashion based on the smoothed user-item matrix. Experiments on two real-world collaborative tagging datasets prove the effectiveness of our approach.
AB - Collaborative tagging sites allow users to save and annotate their favorite Web contents with tags. These tags provide a novel source of information for collaborative filtering. However, major gap exists in how to integrate tagging information into traditional recommender systems for better recommendation quality, due to the difficulty to quantize these semantic tags. This paper proposes a novel approach to convert this semantic information into quantitative values from a smoothing point of view, taking advantage of the topicbased method, and then make recommendation in a traditional user-based CF fashion based on the smoothed user-item matrix. Experiments on two real-world collaborative tagging datasets prove the effectiveness of our approach.
KW - Collaborative filtering
KW - Recommender systems
KW - Smoothing
KW - Tag
KW - Weighing
UR - http://www.scopus.com/inward/record.url?scp=77957804296&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77957804296&partnerID=8YFLogxK
U2 - 10.1109/SOLI.2010.5551603
DO - 10.1109/SOLI.2010.5551603
M3 - Conference contribution
AN - SCOPUS:77957804296
SN - 9781424471188
SP - 452
EP - 456
BT - Proceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010
ER -