Construction of recommender system based on cognitive model for "self-reflection"

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Every human processes a set of mental schemas for problem solving. We develop and improve these schemas by reflecting on our experiences with errors, which is a type of metacognition (Kayashima, 2008). In this study, we proposed a cognitive model of this "self-reflection" process based on Kayashima's two-layer working memory model, and developed a food recommender system using our cognitive model. In the test simulation, the users were satisfied with the foods that the system recommended, although the recommendation results were unexpected to the users. This implied the system practically worked to satisfy the user's expectation. On the other hand, the candidate recommendations which the system selected as its final output were different from those provided by the users. This suggests that the cognitive model needs improvement in terms of psychological reality.

    Original languageEnglish
    Title of host publicationHAI 2017 - Proceedings of the 5th International Conference on Human Agent Interaction
    PublisherAssociation for Computing Machinery, Inc
    Pages517-521
    Number of pages5
    ISBN (Electronic)9781450351133
    DOIs
    Publication statusPublished - 2017 Oct 17
    Event5th International Conference on Human Agent Interaction, HAI 2017 - Bielefeld, Germany
    Duration: 2017 Oct 172017 Oct 20

    Other

    Other5th International Conference on Human Agent Interaction, HAI 2017
    CountryGermany
    CityBielefeld
    Period17/10/1717/10/20

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    Keywords

    • Cognitive model
    • Meta-cognition
    • Recommender system
    • Self-reflection

    ASJC Scopus subject areas

    • Human-Computer Interaction

    Cite this

    Tawatsuji, Y., Yasuda, Y., & Matsui, T. (2017). Construction of recommender system based on cognitive model for "self-reflection". In HAI 2017 - Proceedings of the 5th International Conference on Human Agent Interaction (pp. 517-521). Association for Computing Machinery, Inc. https://doi.org/10.1145/3125739.3132612