Visual Interfaces for Recommendation Systems: Finding Similar and Dissimilar Peers

作     者：Du, Fan Plaisant, Catherine Spring, Neil Shneiderman, Ben 

作者机构：Univ Maryland Dept Comp Sci AV Williams Bldg8223 Pain Branch Dr College Pk MD 20742 USA 

出 版 物：《ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY》 (美国计算机学会智能系统和技术汇刊)

年 卷 期：2019年第10卷第1期

页      面：9-9页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Adobe Research 

主　　题：Similarity personal record multidimensional data visualization temporal visualization decision making visual analytics 

摘      要：Recommendation applications can guide users in making important life choices by referring to the activities of similar peers. For example, students making academic plans may learn from the data of similar students, while patients and their physicians may explore data from similar patients to select the best treatment. Selecting an appropriate peer group has a strong impact on the value of the guidance that can result from analyzing the peer group data. In this article, we describe a visual interface that helps users review the similarity and differences between a seed record and a group of similar records and refine the selection. We introduce the LikeMeDonuts, Ranking Glyph, and History Heatmap visualizations. The interface was refined through three rounds of formative usability evaluation with 12 target users, and its usefulness was evaluated by a case study with a student review manager using real student data. We describe three analytic workflows observed during use and summarize how users input shaped the final design.