Name
Abstract | ||
|---|---|---|
In the application of Web 2.0, some websites usually give the list of something popular for their users. To reach this, they first collect ratings on something from a large number users, and then perform the calculation through some algorithms. The algorithms, however, don't take the credibility of user himself into consideration. The paper proposes a ranking model based on user's credit, which takes user's credit as his weight integrated into his rating, and thus information submitted by different users has different effectiveness. The steps to implement this is firstly to cluster users by K-means to find out senior users, then to evaluate something synthetically by Attribution Coordinate Synthetic Evaluation on condition that senior users' rating is weighted, and finally to get ranking list. The simulation for film recommendation validates the model for recommendation system. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/GrC.2012.6468700 | GrC |
Keywords | Field | DocType |
different effectiveness,synthetic evaluation,cluster user,k-means,ranking model,large number user,senior user,film recommendation,ranking list,recommendation system,recommendation ranking,collect rating,recommendation ranking model,different user,user interfaces,internet,learning artificial intelligence,recommender systems | Recommender system,k-means clustering,World Wide Web,Credibility,Ranking,Pattern clustering,Computer science,Attribution,User interface,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-4673-2310-9 | 1 | 0.43 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolin Xu | 1 | 268 | 25.21 |
| Guanglin Xu | 2 | 26 | 9.95 |