Tutorials for Recommender Systems:

1.Implementing your own recommender systems in python

2.Beginners‘ guide to Non-negative Matrix Factorization

3.Alternating Least Square Method for Collabrative Filtering

4.An introductory Recommender System Tutorial

5.A gentle introduction to recommender systems with implicit feedback

6.An inplicit feedback recommender for the movielens dataset

7.Recommending Movies at scale(Python)

Websites of datasets:

1.Four Square Data Set

2.Movielens Dataset

3.Kaggle Dataset(seems something is wrong with it....)

Some Materials on Github about Tensor Flow 

1.TensorFlow Examples

2.This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code and solutions to the exercises in my O‘Reilly book Hands-on Machine Learning with Scikit-Learn and TensorFlow

3.These tutorials are intended for beginners in Deep Learning and TensorFlow

Things about WARP theory:

1.Github link: warp.py

2.https://stats.stackexchange.com/questions/141634/implementing-warp-loss-gradient-computation/189811

3.warp-loss-for-implicit-feedback-recommendation

Some thing about data splitting:

https://stats.stackexchange.com/questions/146689/how-to-divide-dataset-into-training-and-test-set-in-recommender-systems

To Be Added...

Links of All in Twente（Updatesd Aug,7th）