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.
翻译:由于对可预测和问责的人工情报的需求日益增加,通过具体说明如何建议项目或建议系统为何相关,解释或说明建议系统结果或说明建议系统结果的理由的能力日益成为首要目标,然而,目前的模型并不明确代表用户在与项目的总体互动期间,从选择到使用,在选择项目到使用的整个互动过程中可能遇到的服务和行为者,因此它们无法评估其对用户经验的影响。为了解决这一问题,我们提出一种新的解释方法,即使用服务模式,以便(一) 从审查中提取不同微粒级项目互动各个阶段的经验数据,并(二) 围绕这些阶段组织建议的理由。在用户研究中,我们将我们的方法与反映建议系统结果的合理性的基线进行比较。与会者评价了我们基于服务的理由模型所提供的了解用户认识支持比基线所提供的支持要高。此外,我们的模型得到不同水平的Curiosity 适足性和满意度和满意度评估,以及(NfC) 围绕这些阶段安排,不同,高级NfC参与者建议采用反映建议个人互动调查结果的基线。