P3 adoption README
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Environment:
   Ubuntu 14.04

Files:

├── AutoEncoder          : Neural Network with Auto Encoder
├── baseline             : TA's baseline model
├── Hottest              : Hottest Recommendation Model
├── lcd                  : Local Community Detection Model
├── mf                   : Matrix Factorization Model
├── NeuralNetwork        : Neural Network Model
├── svd                  : Singular Value Decomposition
│
├── eval_k_precision.py
├── graph.txt
├── ReadMe               : the Readme file
├── PPT.pdf              : the slides
├── Report.pdf           : the report.
├── test_data
│   ├── test_data_a1.txt
│   ├── test_data_a2.txt
│   ├── test_data_a3.txt
│   ├── test_data_q1.txt
│   ├── test_data_q2.txt
│   └── test_data_q3.txt
├── training.txt
└── valid_data
    ├── valid_data_q1.txt
    ├── valid_data_q2.txt
    └── valid_data_q3.txt




How to execute: 
   Enter each directory and type the following command.

   make; make run; make test

   the above command should compile the code and execute 
   to produce correct answers.

   Some model uses additional library that required to install
   before compilation. We use arrayfire(http://arrayfire.com/)
   to speed up computation through GPU computation, use libmf
   (http://www.csie.ntu.edu.tw/~cjlin/libmf/) to do matrix
   factorization, we also uses Matlab to do Singular Value 
   Decomposition, and python to evaluate the performance. The
   above tools are required to run all our codes.