Name
Title | ||
|---|---|---|
Implementing Personalized Web News Delivery Service Using Tales Of Familiar Framework |
Abstract | ||
|---|---|---|
We have previously proposed the framework of Tales of Familiar (ToF), where an agent (called familiar) autonomously delivers information from various data streams as exclusively personalized tales for individual users. Based on the ToF framework, this paper implements a news delivery service, where a stuffed doll (as a familiar) tells a user the latest and personally selected news headlines, by matching user's interests with Web news resources. In the implementation, we especially address three challenges: duplication of tales, value estimation of tales, and delivery timing of tales. We deploy the service in an actual household. The empirical result shows that the subject felt it useful that the familiar pushed his interesting news, automatically. We also evaluate how much the developed service was able to cover the technical issues. |
Year | Venue | Field |
|---|---|---|
2018 | 2018 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS) | Data stream mining,World Wide Web,Web news,Computer science,Computer network,Delivery timing |
DocType | ISSN | Citations |
Conference | 2474-2503 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kentaro Noda | 1 | 2 | 1.12 |
| Yoshihiro Wada | 2 | 0 | 0.34 |
| Sachio Saiki | 3 | 55 | 24.46 |
| Masahide Nakamura | 4 | 526 | 72.51 |
| Kiyoshi Yasuda | 5 | 1 | 4.75 |