Abstract
Movie recommendation is a very popular service in internet based movie related websites such as NetFlix, MovieLens. The performance of the recommendation plays the key role in the user experience. Existing works have shown that combining content based and collaborative filtering based algorithms is the best way for movie recommendation. Nevertheless, the performance of this hybrid algorithm is strongly depended on the strategy how to combine the basic pure algorithms. Existing works usually use a static combination strategy which may generate even worse performance for some users. To solve this problems, in this paper we propose a new item based hybrid algorithm that uses a dynamic user adaptive combination strategy. Besides, we also exploit the external open resources IMDB as the movie content data. Experiments on real datasets show that the dynamic user adaptive combination strategy can significantly enhance the performance of the recommendation and the external open resource IMDB is a very good information resource for recommendation.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012 |
| Pages | 172-176 |
| Number of pages | 5 |
| DOIs | |
| State | Published - 2012 |
| Externally published | Yes |
| Event | 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012 - Suzhou, China Duration: Jul 8 2012 → Jul 10 2012 |
Other
| Other | 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012 |
|---|---|
| Country | China |
| City | Suzhou |
| Period | 7/8/12 → 7/10/12 |
Fingerprint
ASJC Scopus subject areas
- Strategy and Management
- Information Systems
Cite this
A dynamic user adaptive combination strategy for hybrid movie recommendation. / Chen, Cai; Zeng, Dajun.
Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012. 2012. p. 172-176 6273525.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A dynamic user adaptive combination strategy for hybrid movie recommendation
AU - Chen, Cai
AU - Zeng, Dajun
PY - 2012
Y1 - 2012
N2 - Movie recommendation is a very popular service in internet based movie related websites such as NetFlix, MovieLens. The performance of the recommendation plays the key role in the user experience. Existing works have shown that combining content based and collaborative filtering based algorithms is the best way for movie recommendation. Nevertheless, the performance of this hybrid algorithm is strongly depended on the strategy how to combine the basic pure algorithms. Existing works usually use a static combination strategy which may generate even worse performance for some users. To solve this problems, in this paper we propose a new item based hybrid algorithm that uses a dynamic user adaptive combination strategy. Besides, we also exploit the external open resources IMDB as the movie content data. Experiments on real datasets show that the dynamic user adaptive combination strategy can significantly enhance the performance of the recommendation and the external open resource IMDB is a very good information resource for recommendation.
AB - Movie recommendation is a very popular service in internet based movie related websites such as NetFlix, MovieLens. The performance of the recommendation plays the key role in the user experience. Existing works have shown that combining content based and collaborative filtering based algorithms is the best way for movie recommendation. Nevertheless, the performance of this hybrid algorithm is strongly depended on the strategy how to combine the basic pure algorithms. Existing works usually use a static combination strategy which may generate even worse performance for some users. To solve this problems, in this paper we propose a new item based hybrid algorithm that uses a dynamic user adaptive combination strategy. Besides, we also exploit the external open resources IMDB as the movie content data. Experiments on real datasets show that the dynamic user adaptive combination strategy can significantly enhance the performance of the recommendation and the external open resource IMDB is a very good information resource for recommendation.
UR - http://www.scopus.com/inward/record.url?scp=84867210700&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867210700&partnerID=8YFLogxK
U2 - 10.1109/SOLI.2012.6273525
DO - 10.1109/SOLI.2012.6273525
M3 - Conference contribution
AN - SCOPUS:84867210700
SN - 9781467324007
SP - 172
EP - 176
BT - Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012
ER -