Understanding Cooperative Learning in Context-aware Recommender Systems

A User-system Interaction Perspective

Na Jiang, Chee-Wee Tan, Weiquan Wang, Hefu Liu, Jibao Gu

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

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Resumé

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.
OriginalsprogEngelsk
TitelICIS 2017 Proceedings
Udgivelses stedAtlanta, GA
ForlagAssociation for Information Systems. AIS Electronic Library (AISeL)
Publikationsdato2017
Sider11
StatusUdgivet - 2017
Begivenhed38th International Conference on Information Systems 2017: Transforming Society with Digital Innovation - Coex Convention Center , Seoul, Sydkorea
Varighed: 10 dec. 201713 dec. 2017
Konferencens nummer: 38
https://icis2017.aisnet.org/

Konference

Konference38th International Conference on Information Systems 2017
Nummer38
LokationCoex Convention Center
LandSydkorea
BySeoul
Periode10/12/201713/12/2017
Internetadresse
NavnProceedings of the International Conference on Information Systems
ISSN0000-0033

Emneord

  • Context-aware
  • Recommender systems
  • Cooperative learning
  • User-system interaction
  • Human-machine relationships

Citer dette

Jiang, N., Tan, C-W., Wang, W., Liu, H., & Gu, J. (2017). Understanding Cooperative Learning in Context-aware Recommender Systems: A User-system Interaction Perspective. I ICIS 2017 Proceedings (s. 11). Atlanta, GA: Association for Information Systems. AIS Electronic Library (AISeL). Proceedings of the International Conference on Information Systems
Jiang, Na ; Tan, Chee-Wee ; Wang, Weiquan ; Liu, Hefu ; Gu, Jibao. / Understanding Cooperative Learning in Context-aware Recommender Systems : A User-system Interaction Perspective. ICIS 2017 Proceedings. Atlanta, GA : Association for Information Systems. AIS Electronic Library (AISeL), 2017. s. 11 (Proceedings of the International Conference on Information Systems).
@inproceedings{75202b86a11e45ea814fe6270d418888,
title = "Understanding Cooperative Learning in Context-aware Recommender Systems: A User-system Interaction Perspective",
abstract = "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.",
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author = "Na Jiang and Chee-Wee Tan and Weiquan Wang and Hefu Liu and Jibao Gu",
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Jiang, N, Tan, C-W, Wang, W, Liu, H & Gu, J 2017, Understanding Cooperative Learning in Context-aware Recommender Systems: A User-system Interaction Perspective. i ICIS 2017 Proceedings. Association for Information Systems. AIS Electronic Library (AISeL), Atlanta, GA, Proceedings of the International Conference on Information Systems, s. 11, 38th International Conference on Information Systems 2017, Seoul, Sydkorea, 10/12/2017.

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. s. 11 (Proceedings of the International Conference on Information Systems).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

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T1 - Understanding Cooperative Learning in Context-aware Recommender Systems

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AU - Gu, Jibao

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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.

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Jiang N, Tan C-W, Wang W, Liu H, Gu J. Understanding Cooperative Learning in Context-aware Recommender Systems: A User-system Interaction Perspective. I ICIS 2017 Proceedings. Atlanta, GA: Association for Information Systems. AIS Electronic Library (AISeL). 2017. s. 11. (Proceedings of the International Conference on Information Systems).