Analysis and evaluation of recommendation systems

Authors:
Emiko Orimo;Hideki Koike;Toshiyuki Masui;Akikazu Takeuchi
Affiliations:
So-net Entertainment Corporation and University of Electro-Communications;University of Electro-Communications;Apple Computer Inc.;So-net Entertainment Corporation
Venue:
Proceedings of the 2007 conference on Human interface: Part I
Year:
2007

Citing 4
Cited 0

Amazon.com Recommendations: Item-to-Item Collaborative Filtering

IEEE Internet Computing
Visualizing Document Space by Force-directed Dynamic Layout

VL '97 Proceedings of the 1997 IEEE Symposium on Visual Languages (VL '97)
ZASH: A Browsing System for Multi-Dimensional Data

VL '99 Proceedings of the IEEE Symposium on Visual Languages
Enhancing digital libraries with TechLens+

Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries

Quantified Score

Hi-index	0.00

Visualization

Abstract

Popular online services, such as Amazon.com, provide recommendations for users by using other users' rating scores for items. In this study, we describe three types of rating systems: score-rated, count-rated, and digital-rated. We hypothesize that digital-rated systems provide the most useful recommendations. Then we analyze the differences in the results of the rating when the granularity of the score changes. Finally, we visualize users by developing a 2-D visualization system that uses a multi-dimensional scaling method.