Abstract
| Original language | English |
|---|---|
| Title of host publication | ICIS 2017 Proceedings |
| Place of Publication | Atlanta, GA |
| Publisher | Association for Information Systems. AIS Electronic Library (AISeL) |
| Publication date | 2017 |
| Pages | 11 |
| Publication status | Published - 2017 |
| Event | ICIS 2017: Transforming Society with Digital Innovation - Coex Convention Center , Seoul, Korea, Republic of Duration: 10 Dec 2017 → 13 Dec 2017 Conference number: 38 https://icis2017.aisnet.org/ |
Conference
| Conference | ICIS 2017 |
|---|---|
| Number | 38 |
| Location | Coex Convention Center |
| Country | Korea, Republic of |
| City | Seoul |
| Period | 10/12/2017 → 13/12/2017 |
| Internet address |
| Series | Proceedings of the International Conference on Information Systems |
|---|---|
| ISSN | 0000-0033 |
Keywords
- Context-aware
- Recommender systems
- Cooperative learning
- User-system interaction
- Human-machine relationships
Cite this
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Understanding Cooperative Learning in Context-aware Recommender Systems : A User-system Interaction Perspective. / Jiang, Na; Tan, Chee-Wee; Wang, Weiquan; Liu, Hefu; Gu, Jibao.
ICIS 2017 Proceedings. Atlanta, GA : Association for Information Systems. AIS Electronic Library (AISeL), 2017. p. 11 (Proceedings of the International Conference on Information Systems).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Understanding Cooperative Learning in Context-aware Recommender Systems
T2 - A User-system Interaction Perspective
AU - Jiang, Na
AU - Tan, Chee-Wee
AU - Wang, Weiquan
AU - Liu, Hefu
AU - Gu, Jibao
PY - 2017
Y1 - 2017
N2 - Context-Aware Recommender Systems (CARSs) are becoming commonplace. Yet, there is a paucity of studies that investigates how such systems could affect usage behavior from a user-system interaction perspective. Building on the Social Interdependence Theory (SIT), we construct a research model that posits cooperative learning as a trait of users’ interactions with CARSs and outline a proposed empirical study for validating the hypothesized relationships in this model. Specifically, we draw on interdependencies in human-machine relationships to postulate positive interdependence as an antecedent of users’ promotive interaction with CARSs, which in turn, dictates the performance of such recommender systems. Furthermore, we introduce scrutability features as design interventions that can be harnessed by developers to mitigate the impact of users’ promotive interaction on the performance of CARSs.
AB - Context-Aware Recommender Systems (CARSs) are becoming commonplace. Yet, there is a paucity of studies that investigates how such systems could affect usage behavior from a user-system interaction perspective. Building on the Social Interdependence Theory (SIT), we construct a research model that posits cooperative learning as a trait of users’ interactions with CARSs and outline a proposed empirical study for validating the hypothesized relationships in this model. Specifically, we draw on interdependencies in human-machine relationships to postulate positive interdependence as an antecedent of users’ promotive interaction with CARSs, which in turn, dictates the performance of such recommender systems. Furthermore, we introduce scrutability features as design interventions that can be harnessed by developers to mitigate the impact of users’ promotive interaction on the performance of CARSs.
KW - Context-aware
KW - Recommender systems
KW - Cooperative learning
KW - User-system interaction
KW - Human-machine relationships
KW - Context-aware
KW - Recommender systems
KW - Cooperative learning
KW - User-system interaction
KW - Human-machine relationships
M3 - Article in proceedings
SP - 11
BT - ICIS 2017 Proceedings
PB - Association for Information Systems. AIS Electronic Library (AISeL)
CY - Atlanta, GA
ER -