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

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Information Systems Pub Date : 2023-12-12 DOI:10.1007/s10844-023-00835-8
Cataldo Musto, Alessandro Francesco Maria Martina, Andrea Iovine, Fedelucio Narducci, Marco de Gemmis, Giovanni Semeraro
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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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告诉我你喜欢什么:在电影虚拟助手中引入自然语言偏好激发策略
偏好提取是每一种推荐算法的关键步骤。在本文中,我们提出了一种策略,允许用户通过自然语言语句表达他们的偏好和需求。特别是,我们的自然语言偏好引出管道允许用户表达对客观电影特征(例如,演员,导演等)以及通过挖掘用户编写的电影评论收集的主观特征的偏好。为了验证我们的说法,我们在电影领域(\(N=114\))进行了一项用户研究。我们实验的主要发现是,用户倾向于使用客观特征来表达他们的偏好,客观特征的使用在很大程度上超过了主观特征的使用,主观特征的表达更加复杂。然而,当用户也能够在主观特征方面表达他们的偏好时,他们会在更少的会话回合中获得更好的推荐。我们还确定了用户通过使用主观特征与虚拟助手交谈时出现的主要挑战,这为我们方法的未来发展铺平了道路。
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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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