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Linguistics-based dialogue simulations to evaluate argumentative conversational recommender systems.

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Title: Linguistics-based dialogue simulations to evaluate argumentative conversational recommender systems.
Authors: Di Bratto, Martina1,2 (AUTHOR) martina.dibratto@unina.it, Origlia, Antonio1 (AUTHOR), Di Maro, Maria1 (AUTHOR), Mennella, Sabrina2,3 (AUTHOR)
Source: User Modeling & User-Adapted Interaction. Nov2024, Vol. 34 Issue 5, p1581-1611. 31p.
Subjects: Linguistic models, Pragmatics, Deliberation, Argument, Recommender systems
Abstract: Conversational recommender systems aim at recommending the most relevant information for users based on textual or spoken dialogues, through which users can communicate their preferences to the system more efficiently. Argumentative conversational recommender systems represent a kind of deliberation dialogue in which participants share their specific beliefs in the respective representations of the common ground, to act towards a common goal. The goal of such systems is to present appropriate supporting arguments to their recommendations to show the interlocutor that a specific item corresponds to their manifested interests. Here, we present a cross-disciplinary argumentation-based conversational recommender model based on cognitive pragmatics. We also present a dialogue simulator to investigate the quality of the theoretical background. We produced a set of synthetic dialogues based on a computational model implementing the linguistic theory and we collected human evaluations about the plausibility and efficiency of these dialogues. Our results show that the synthetic dialogues obtain high scores concerning their naturalness and the selection of the supporting arguments. [ABSTRACT FROM AUTHOR]
Copyright of User Modeling & User-Adapted Interaction is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Conversational recommender systems aim at recommending the most relevant information for users based on textual or spoken dialogues, through which users can communicate their preferences to the system more efficiently. Argumentative conversational recommender systems represent a kind of deliberation dialogue in which participants share their specific beliefs in the respective representations of the common ground, to act towards a common goal. The goal of such systems is to present appropriate supporting arguments to their recommendations to show the interlocutor that a specific item corresponds to their manifested interests. Here, we present a cross-disciplinary argumentation-based conversational recommender model based on cognitive pragmatics. We also present a dialogue simulator to investigate the quality of the theoretical background. We produced a set of synthetic dialogues based on a computational model implementing the linguistic theory and we collected human evaluations about the plausibility and efficiency of these dialogues. Our results show that the synthetic dialogues obtain high scores concerning their naturalness and the selection of the supporting arguments. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of User Modeling & User-Adapted Interaction is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11257-024-09403-3
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      – Code: eng
        Text: English
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        Type: general
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      – SubjectFull: Deliberation
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      – SubjectFull: Argument
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            – D: 01
              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
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