Name
Abstract | ||
|---|---|---|
In this paper, we present a set recommendation framework that proposes sets of items, whereas conventional recommendation methods recommend each item independently. Our new approach to the set recommendation framework can propose sets of items on the basis on the user's initially chosen set. In this approach, items are added to or deleted from the initial set so that the modified set matches the target classification. Since the data sets created by the latest applications can be quite large, we use ZDD (Zero-suppressed Binary Decision Diagram) to make the searching more efficient. This framework is applicable to a wide range of applications such as advertising on the Internet and healthy life advice based on personal lifelog data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-30217-6_16 | PAKDD |
Keywords | Field | DocType |
modified set,conventional recommendation method,initial set,class difference,personal lifelog data,target classification,healthy life advice,zero-suppressed binary decision diagram,new approach,incremental set recommendation,set recommendation framework,latest application,classification,collaborative filtering | Data mining,Lifelog,Data set,Collaborative filtering,Information retrieval,Computer science,Binary decision diagram,Class differences,The Internet | Conference |
Citations | PageRank | References |
2 | 0.40 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yasuyuki Shirai | 1 | 10 | 4.98 |
| Koji Tsuruma | 2 | 14 | 1.43 |
| Yuko Sakurai | 3 | 201 | 31.37 |
| Satoshi Oyama | 4 | 265 | 34.67 |
| Shin-ichi Minato | 5 | 725 | 84.72 |