Name
Abstract | ||
|---|---|---|
The authors aim to develop a system that saves money by providing tips (proposed actions; the tips are called know-how) based on user preferences. In order to select helpful tips, this study develops a method that improves estimation accuracy by optimizing the tip indices that represent the tendency in user preferences. Concretely, the method optimizes the tip indices using both the result generally acquired by five-level evaluations and the result gradually acquired by a paired comparison method in which the users are requested to select one of pair of tips. Evaluation experiments show the general trend that estimation accuracy rises with the amounts of paired comparison data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/AINA.2018.00082 | 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA) |
Keywords | Field | DocType |
Pairwise preferences,Money saving tips,Recommender system | Recommender system,Pairwise comparison,Data mining,Computer science,Euclidean distance,Distributed computing | Conference |
ISSN | ISBN | Citations |
1550-445X | 978-1-5386-2196-7 | 0 |
PageRank | References | Authors |
0.34 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takashi Araki | 1 | 5 | 1.52 |
| Chiaki Doi | 2 | 0 | 1.01 |
| Daizo Ikeda | 3 | 8 | 8.59 |
| Masaji Katagiri | 4 | 42 | 5.95 |
| Shuhei Kuwata | 5 | 0 | 0.34 |
| Tatsuya Kawasaki | 6 | 0 | 0.34 |