Hermes is Lab41's foray into recommender systems. It explores how to choose a recommender system for a new application by analyzing the performance of multiple recommender system algorithms on a variety of datasets.
It also explores how recommender systems may assist a software developer of data scientist find new data, tools, and computer programs.
This readme will be updated as the project progresses so stay tuned!
##Blog Overviews
| Join the Hermes Running Club | March 2016 |
| Python2Vec: Word Embeddings for Source Code | March 2016 |
| TPS Report for Recommender Systems? Traditional Performance Metrics | March 2016 |
| Recommender Systems - It's Not All About the Accuracy | January 2016 |
| The Nine Must-Have Datasets for Investigating Recommender Systems | February 2016 |
| Recommending Recommendation Systems (project intro) | December 2015 |
We are trying varied tools and concepts to visualize the results of this project.
conda install bokeh- from top-level hermes folder
$bokeh serve src/results/hermes_run_view.py - view in browser at
http://localhost:5006/hermes_run_view
easy_install web.py- from viz folder
$python app.py - view in browser from location:port displayed in terminal
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent