Name
Title | ||
|---|---|---|
Cellular phone ringing tone recommendation system based on collaborative filtering method |
Abstract | ||
|---|---|---|
We have developed a prototype of cellular phone ringing tome recommendation system using memory-based collaborative filtering and we have carried out examinations to evaluate its performance. The ringing tone content was stored on a server from where the users were able to download the desired items according to their preferences. An extensive log data accumulated at the download service site for a fixed period of time was used. The log data contained only information for the users' downloaded ringing tomes without evaluation data. The user set and the tone downloadable content set were not fixed and our goal was to investigate how collaborative filtering could be successfully applied to a system with such continuously changing conditions. The Jaccard's similarity coefficient was used to calculate the similarity between the users. The learning period, the recommendation period and the number of the similar users were used as condition parameters. The system quality evaluation showed that the recall increases with the increase of the learning period but decreases with the increase of the recommendation period. Optimal values for the number of the most similar users as well as for the learning and the recommendation periods were experimentally obtained. It was shown that the collaborative filtering method could be successfully applied to a cellular phone ringing tone recommendation system. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/CIRA.2003.1222119 | Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium |
Keywords | Field | DocType |
cellular radio,information retrieval,learning systems,mobile handsets,cellular phone,download service,learning period,log data,memory based collaborative filtering method,ringing tone recommendation system,server | Recommender system,Collaborative filtering,Cellular radio,Information retrieval,Computer science,Ringing,Download,Phone,Artificial intelligence,Jaccard index,Multimedia,Machine learning | Conference |
Volume | ISBN | Citations |
1 | 0-7803-7866-0 | 4 |
PageRank | References | Authors |
0.44 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vlaho Kostov | 1 | 14 | 6.24 |
| Eiichi Naito | 2 | 14 | 3.64 |
| Jun Ozawa | 3 | 21 | 12.26 |