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Context-Aware Explanations in Recommender Systems

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Progresses in Artificial Intelligence & Robotics: Algorithms & Applications (ICDLAIR 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 441))

Abstract

Recommender systems aim to help users find relevant items more quickly by providing personalized recommendations. Explanations in recommender systems help users understand why such recommendations have been generated, which in turn makes the system more transparent and promotes users’ trust and satisfaction. In recent years, explaining recommendations has drawn increasing attention from both academia and from industry. In this paper, we present a user study to investigate context-aware explanations in recommender systems. In particular, we build a web-based questionnaire that is able to interact with users: generating and explaining recommendations. With this questionnaire, we investigate the effects of context-aware explanations in terms of efficiency, effectiveness, persuasiveness, satisfaction, trust and transparency through a user study.

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Authors and Affiliations

  1. Paris-Dauphine University, PSL Research University, CNRS UMR 7243, LAMSADE, 75016, Paris, France

    Jinfeng Zhong & Elsa Negre

Authors
  1. Jinfeng Zhong
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  2. Elsa Negre
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Corresponding author

Correspondence to Jinfeng Zhong .

Editor information

Editors and Affiliations

  1. Department of Management and Innovation Systems, University of Salerno, Salerno, Italy

    Luigi Troiano

  2. Department of Engineering, University of Sannio, Benevento, Italy

    Alfredo Vaccaro

  3. Department of Computer Science, Central University of Rajasthan, Ajmer, Rajasthan, India

    Nishtha Kesswani

  4. Department of Computer Science, University of Oviedo, Gijón, Spain

    Irene Díaz Rodriguez

  5. EMLYON Business School, Écully, France

    Imene Brigui

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Zhong, J., Negre, E. (2022). Context-Aware Explanations in Recommender Systems. In: Troiano, L., Vaccaro, A., Kesswani, N., Díaz Rodriguez, I., Brigui, I. (eds) Progresses in Artificial Intelligence & Robotics: Algorithms & Applications. ICDLAIR 2021. Lecture Notes in Networks and Systems, vol 441. Springer, Cham. https://doi.org/10.1007/978-3-030-98531-8_8

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