Paper Info
Title
A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering.
Abstract
While conventional Recommender Systems perform well in automatically generating personalized suggestions, it is often difficult for users to understand why certain items are recommended and which parts of the item space are covered by the recommendations. Also, the available means to influence the process of generating results are usually very limited. To alleviate these problems, we suggest a 3D map-based visualization of the entire item space in which we position and present sample items along with recommendations. The map is produced by mapping latent factors obtained from Collaborative Filtering data onto a 2D surface through Multidimensional Scaling. Then, areas that contain items relevant with respect to the current user's preferences are shown as elevations on the map, areas of low interest as valleys. In addition to the presentation of his or her preferences, the user may interactively manipulate the underlying profile by raising or lowering parts of the landscape, also at cold-start. Each change may lead to an immediate update of the recommendations. Using a demonstrator, we conducted a user study that, among others, yielded promising results regarding the usefulness of our approach.
Year
DOI
Venue
2017
10.1145/3025171.3025189
IUI
Keywords
Field
DocType
Recommender Systems, Interactive Recommending, Matrix Factorization, User Interfaces, User Profiles, User Experience, 3D Visualizations
Recommender system,User experience design,Collaborative filtering,Multidimensional scaling,Computer science,Visualization,Matrix decomposition,Human–computer interaction,User interface,Multimedia
Conference
Citations 
PageRank 
References 
8
0.52
46
Authors
3
Name
Order
Citations
PageRank
Johannes Kunkel1122.26
Benedikt Loepp28810.71
Jürgen Ziegler31028300.31