Personalized explanations for hybrid recommender systems

Pigi Kouki, J. Schaffer, J. Pujara, J. O'Donovan, L. Getoor
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引用次数: 119

Abstract

Recommender systems have become pervasive on the web, shaping the way users see information and thus the decisions they make. As these systems get more complex, there is a growing need for transparency. In this paper, we study the problem of generating and visualizing personalized explanations for hybrid recommender systems, which incorporate many different data sources. We build upon a hybrid probabilistic graphical model and develop an approach to generate real-time recommendations along with personalized explanations. To study the benefits of explanations for hybrid recommender systems, we conduct a crowd-sourced user study where our system generates personalized recommendations and explanations for real users of the last.fm music platform. We experiment with 1) different explanation styles (e.g., user-based, item-based), 2) manipulating the number of explanation styles presented, and 3) manipulating the presentation format (e.g., textual vs. visual). We apply a mixed model statistical analysis to consider user personality traits as a control variable and demonstrate the usefulness of our approach in creating personalized hybrid explanations with different style, number, and format.
混合推荐系统的个性化解释
推荐系统在网络上已经无处不在,塑造了用户查看信息的方式,从而影响了他们的决策。随着这些系统变得越来越复杂,对透明度的需求也越来越大。在本文中,我们研究了混合推荐系统的生成和可视化个性化解释的问题,该系统包含许多不同的数据源。我们建立了一个混合概率图形模型,并开发了一种方法来生成实时推荐和个性化解释。为了研究解释对混合推荐系统的好处,我们进行了一项众包用户研究,我们的系统为最后的真实用户生成个性化的推荐和解释。调频音乐平台。我们尝试了1)不同的解释风格(例如,基于用户的,基于项目的),2)操纵呈现的解释风格的数量,以及3)操纵呈现格式(例如,文本与视觉)。我们采用混合模型统计分析,将用户个性特征作为控制变量,并展示了我们的方法在创建具有不同风格、数量和格式的个性化混合解释方面的实用性。
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