Skip to content

usr-av/strata-conference-ca-2018

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Deep Learning Based Search and Recommendation System

Strata Conference , March - 2018, San Jose

Presenters


Session Content

  1. Slides [ PDF : https://github.com/meabhishekkumar/strata-conference-ca-2018/blob/master/deep_learning_based_search_and_recommender_system.pdf ]
  2. Notebooks

Setting up the Enviornment

You can easily setup the enviornment with all required components ( data and notebooks ) with the help of Docker.

Here are the steps.

  1. Install Docker on your local machine. You will required documentation on Docker website [ https://docs.docker.com/install/ ]

  2. Make sure Docker is working fine. If you are not getting any error and able to see the docker

$ docker --version
  1. Download the docker image and create container for the tutorial
$ docker run -it --rm -p 8888:8888 -p 0.0.0.0:6006:6006 meabhishekkumar/strata-ca-2018

Reference Papers

  1. Restricted Boltzmann Machines for Collaborative Filtering by Ruslan Salakhutdinov. Source: http://www.machinelearning.org/proceedings/icml2007/papers/407.pdf
  2. Wide & Deep Learning for Recommender Systems by Heng-Tze Cheng. Source: https://arxiv.org/abs/1606.07792
  3. A Survey and Critique of Deep Learning on Recommender Systems by Lei Zheng. Source: http://bdsc.lab.uic.edu/docs/survey-critique-deep.pdf
  4. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. IJCAI2017 Source: https://arxiv.org/abs/1703.04247
  5. Deep Neural Networks for YouTube Recommendations by Paul Covington. Source: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf

Credits :

  • Recommendation system notebooks are inspired by Olivier Grisel work using Keras

About

environment setup for strata conference 2018

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 100.0%