Understanding Cooperative Learning in Context-aware Recommender Systems: A User-system Interaction Perspective

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

Understanding Cooperative Learning in Context-aware Recommender Systems: A User-system Interaction Perspective

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

Department of Digitalization

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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.

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	https://icis2017.aisnet.org/

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

Access to Document

http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1322&context=icis2017Licence: Unspecified

Cite this

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. In ICIS 2017 Proceedings (pp. 11). Association for Information Systems. AIS Electronic Library (AISeL). Proceedings of the International Conference on Information Systems http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1322&context=icis2017

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

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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{\textquoteright} 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{\textquoteright} 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{\textquoteright} promotive interaction on the performance of CARSs.",

keywords = "Context-aware, Recommender systems, Cooperative learning, User-system interaction, Human-machine relationships, Context-aware, Recommender systems, Cooperative learning, User-system interaction, Human-machine relationships",

author = "Na Jiang and Chee-Wee Tan and Weiquan Wang and Hefu Liu and Jibao Gu",

year = "2017",

language = "English",

series = "Proceedings of the International Conference on Information Systems",

pages = "11",

booktitle = "ICIS 2017 Proceedings",

publisher = "Association for Information Systems. AIS Electronic Library (AISeL)",

note = "ICIS 2017 : Transforming Society with Digital Innovation ; Conference date: 10-12-2017 Through 13-12-2017",

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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. in ICIS 2017 Proceedings. Association for Information Systems. AIS Electronic Library (AISeL), Atlanta, GA, Proceedings of the International Conference on Information Systems, pp. 11, ICIS 2017, Seoul, Korea, Republic of, 10/12/2017. <http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1322&context=icis2017>

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

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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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