From 0dd09a44010b24dea48ffec135b7d82d52708129 Mon Sep 17 00:00:00 2001 From: Hoai Phuoc Truong Date: Sun, 6 Aug 2017 14:09:14 +0700 Subject: [PATCH] Add about page part to README --- README.md | 48 +++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 47 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 9693939..2d5dfc0 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,53 @@ ICML 2017 website Frontend project repository -Website, detailing the app design: +## Note +This app is in pre-release mode for ICML 2017. Please send feedback +and bug reports to icml17app@gmail.com or file an issue on our github project page. + +## What is this app? +This app helps navigate the rich program of ICML 2017. The app provides an easily +navigable schedule, enables users to quickly find interesting papers, publicly +comment them, register their interest using 'like' and 'bookmark', and browse what +others are interested in. In addition, users' preferences feed a personalized paper +recommendation engine. + +## Bookmarks and Likes. +Papers you choose to bookmark or like are made available through your +"Library" page. + +The difference between bookmarks and likes is that liked papers are fed +into the recommendation engine. + +## Recommendations. +Recommendations are available through the "Feed" page and are updated +every two minutes. On that page recommended papers are preceded by +trending papers which shows some of the most popular papers. + +The recommendation engine is based on the collaborative topic regression (CTR) model of C. Wang and D. Blei. We also used a few tricks to integrate the model with our app. + +## Why are we building it? +There are a few reasons one of which is that it can be challenging to keep up with all of the great work published at conferences. We believe that an easy to use app and a good recommendation engine can help surface interesting papers. + +Moreover, we built this app as a service to the community, we will open source the app. We would like others to use and even build on it. We believe that the app can also be used as a test bed for recommender engines. As such the app keeps detailed (and anonymous) logs for future offline analysis and model comparisons. + +## Who is involved? +The app was designed and built by a team of machine learning students and +researchers spread across England, France, and Canada. + +1. Adrien Todeschini, Scorelab.io +2. Hoai Phuoc Truong, McGill University +3. James Ravenscroft, University of Warwick +4. Laurent Charlin, Université de Montréal (HEC) +5. Lazar Valkov, University of Edinburgh +6. Maria Liakata, University of Warwick, Alan Turing Institute +7. Taimur Abdaal, University of Oxford +8. Valerio Perrone, University of Warwick +9. Yee Whye Teh, University of Oxford, DeepMind, Alan Turing Institute + +# Technical contents section below (for internal development use only): + +## Website, detailing the app design: http://icml2017spec.s3-website-us-west-2.amazonaws.com Start the server