Name
Title | ||
|---|---|---|
A movie rating prediction system of user propensity analysis based on collaborative filtering and fuzzy system |
Abstract | ||
|---|---|---|
Recently, an intelligent system is developed for proper service which isn't passive system. Recent system can answer and recommend to user before user's request. This intelligent system is used for personalized recommendation system and representative techniques are content-based and collaborative filtering. In this study, we propose a prediction system which is based on the techniques of recommendation system using a collaborative filtering and fuzzy system to solve the collaborative filtering problems. In order to verify the prediction system, we used the user's rating data about movies. We predicted the user's rating using this data. The accuracy of this prediction system is determined by computing the predicted RMSE (root mean square error) of the system against the actual rating about the each movie. And predicted RMSE is compared with the existing system. Thus, this prediction system can be applied to base technology of recommendation system and also recommendation of multimedia such as music and books. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/FUZZY.2009.5277415 | FUZZ-IEEE |
Keywords | Field | DocType |
fuzzy system,user propensity analysis,fuzzy reasoning,collaborative filtering,movie rating prediction system,fuzzy rule system,propensity analysis,existing system,information filtering,rating data,cinematography,passive system,content-based filtering,information filters,internet,intelligent system,personalized recommendation system,humanities,recommendation system,root mean square error,prediction system,internet service,content-based retrieval,actual rating,recent system,mean square error methods,collaboration,motion pictures,data mining,training data,fuzzy systems,recommender system,filtering,reliability | Recommender system,Training set,Data mining,Collaborative filtering,Computer science,Filter (signal processing),Mean squared error,Artificial intelligence,Fuzzy control system,Machine learning,Prediction system,The Internet | Conference |
ISSN | ISBN | Citations |
1098-7584 E-ISBN : 978-1-4244-3597-5 | 978-1-4244-3597-5 | 8 |
PageRank | References | Authors |
0.69 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae-Ryong Jeon | 1 | 32 | 7.18 |
| Jaewoo Cho | 2 | 11 | 1.78 |
| Soojin Lee | 3 | 17 | 3.45 |
| Gyeongdong Baek | 4 | 27 | 6.28 |
| Sungshin Kim | 5 | 210 | 64.17 |