Despite the acknowledgment that the perception of explanations may vary considerably between end-users, explainable recommender systems (RS) have traditionally followed a one-size-fits-all model, whereby the same explanation level of detail is provided to each user, without taking into consideration individual user's context, i.e., goals and personal characteristics. To fill this research gap, we aim in this paper at a shift from a one-size-fits-all to a personalized approach to explainable recommendation by giving users agency in deciding which explanation they would like to see. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the recommendations, with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between user's personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation goals. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation goal and user type. Consequently, we suggested some theoretical and design guidelines to support the systematic design of explanatory interfaces in RS tailored to the user's context.
翻译:尽管人们意识到 end-user 之间对解释的感知可能会有很大的差异,但可解释的推荐系统(RS)传统上都采用一种“一刀切”的模式,即向每个用户提供相同的解释程度(详细程度),而不考虑个人用户的背景(即目标和个人特征)。为了填补这一研究空白,本文试图从采用一种个性化的解释方法来实现可解释推荐,为用户在选择想要看到的解释方面提供了机会。我们开发了一个透明的 推荐和兴趣建模应用程序(RIMA),为不同类型的 end-users 提供了三个不同程度的详细程度的个性化解释(基本、中级、高级),以满足不同类型 end-users 的需求。我们进行了一项在主体内部进行的研究(N=31),以研究用户的个人特征和解释的详细程度之间的关系,以及这两个变量对解释 RS 在不同解释目标方面的感知的影响。我们的结果表明,解释 RS 的不同详细程度的感知受到解释目标和用户类型的不同程度的影响。因此,我们提出了一些理论和设计指导方针,以支持根据用户的背景系统地设计 RS 的解释界面。