research-article

Justification of recommender systems results: a service-based approach

Authors:

Zhongli Filippo Hu,

Liliana ArdissonoAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 33, Issue 3

Pages 643 - 685

https://doi.org/10.1007/s11257-022-09345-8

Published: 29 October 2022 Publication History

Abstract

With the increasing demand for predictable and accountable Artificial Intelligence, the ability to explain or justify recommender systems results by specifying how items are suggested, or why they are relevant, has become a primary goal. However, current models do not explicitly represent the services and actors that the user might encounter during the overall interaction with an item, from its selection to its usage. Thus, they cannot assess their impact on the user’s experience. To address this issue, we propose a novel justification approach that uses service models to (i) extract experience data from reviews concerning all the stages of interaction with items, at different granularity levels, and (ii) organize the justification of recommendations around those stages. In a user study, we compared our approach with baselines reflecting the state of the art in the justification of recommender systems results. The participants evaluated the Perceived User Awareness Support provided by our service-based justification models higher than the one offered by the baselines. Moreover, our models received higher Interface Adequacy and Satisfaction evaluations by users having different levels of Curiosity or low Need for Cognition (NfC). Differently, high NfC participants preferred a direct inspection of item reviews. These findings encourage the adoption of service models to justify recommender systems results but suggest the investigation of personalization strategies to suit diverse interaction needs.

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Cited By

Tran TPolat Erdeniz SFelfernig ALubos SEl Mansi MLe V(2024)Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691708(1108-1112)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691708
Geninatti Cossatin ABoratto LGena CMarras MGermanakos PPopescus E(2024)Promoting Green Fashion Consumption in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664922(50-54)Online publication date: 27-Jun-2024
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Hu ZMauro NPetrone GArdissono L(2023)Service-based Presentation of Multimodal Information for the Justification of Recommender Systems ResultsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592962(46-53)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592962

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Information & Contributors

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Published In

cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 33, Issue 3

Jul 2023

147 pages

ISSN:0924-1868

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© The Author(s) 2022.

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Kluwer Academic Publishers

United States

Publication History

Published: 29 October 2022

Accepted: 17 September 2022

Received: 26 May 2022

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University of Torino

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Cited By

Tran TPolat Erdeniz SFelfernig ALubos SEl Mansi MLe V(2024)Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691708(1108-1112)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691708
Geninatti Cossatin ABoratto LGena CMarras MGermanakos PPopescus E(2024)Promoting Green Fashion Consumption in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664922(50-54)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664922
Hu ZMauro NPetrone GArdissono L(2023)Service-based Presentation of Multimodal Information for the Justification of Recommender Systems ResultsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592962(46-53)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592962

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