混合推荐系统的个性化解释

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

摘要

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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personalized explanations for hybrid recommender systems
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.
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