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Journal of Intelligent Information Systems

12.12.2023 | Research

Tell me what you Like: introducing natural language preference elicitation strategies in a virtual assistant for the movie domain

verfasst von: Cataldo Musto, Alessandro Francesco Maria Martina, Andrea Iovine, Fedelucio Narducci, Marco de Gemmis, Giovanni Semeraro

Erschienen in: Journal of Intelligent Information Systems

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Abstract

Preference elicitation is a crucial step for every recommendation algorithm. In this paper, we present a strategy that allows users to express their preferences and needs through natural language statements. In particular, our natural language preference elicitation pipeline allows users to express preferences on objective movie features (e.g., actors, directors, etc.) as well as on subjective features that are collected by mining user-written movie reviews. To validate our claims, we carried out a user study in the movie domain (\(N=114\)). The main finding of our experiment is that users tend to express their preferences by using objective features, whose usage largely overcomes that of subjective features, which are more complicated to be expressed. However, when the users are able to express their preferences also in terms of subjective features, they obtain better recommendations in a lower number of conversation turns. We have also identified the main challenges that arise when users talk to the virtual assistant by using subjective features, and this paves the way for future developments of our methodology.

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Titel: Tell me what you Like: introducing natural language preference elicitation strategies in a virtual assistant for the movie domain
verfasst von: Cataldo Musto
Alessandro Francesco Maria Martina
Andrea Iovine
Fedelucio Narducci
Marco de Gemmis
Giovanni Semeraro
Publikationsdatum: 12.12.2023
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-023-00835-8

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