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

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

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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 languageEnglish
Title of host publicationICIS 2017 Proceedings
Place of PublicationAtlanta, GA
PublisherAssociation for Information Systems. AIS Electronic Library (AISeL)
Publication date2017
Publication statusPublished - 2017
Event38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017: Transforming Society with Digital Innovation - Coex Convention Center , Seoul, Korea, Republic of
Duration: 10 Dec 201713 Dec 2017
Conference number: 38


Conference38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017
LocationCoex Convention Center
Country/TerritoryKorea, Republic of
Internet address
SeriesProceedings of the International Conference on Information Systems


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

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