Name
Title | ||
|---|---|---|
Creating Scalable and Interactive Web Applications Using High Performance Latent Variable Models |
Abstract | ||
|---|---|---|
In this project we outline a modularized, scalable system for comparing Amazon products in an interactive and informative way using efficient latent variable models and dynamic visualization. We demonstrate how our system can build on the structure and rich review information of Amazon products in order to provide a fast, multifaceted, and intuitive comparison. By providing a condensed per-topic comparison visualization to the user, we are able to display aggregate information from the entire set of reviews while providing an interface that is at least as compact as the "most helpful reviews" currently displayed by Amazon, yet far more informative. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Data mining,Computer science,Visualization,Latent variable,Amazon rainforest,Scalable system,Web application,Multimedia,Scalability |
DocType | Volume | Citations |
Journal | abs/1510.06153 | 1 |
PageRank | References | Authors |
0.39 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aaron Q. Li | 1 | 81 | 4.76 |
| yuntian deng | 2 | 241 | 14.12 |
| kublai jing | 3 | 1 | 0.39 |
| joseph w robinson | 4 | 3 | 3.47 |