BookRecommender

##Authors: jf3030, yp2419
##Team ID: 201612-37
##Instruction: Make the recommendation using the ratings given by previous costumers. Datasets and trained models are too big, so they are not listed here. Using Flask to build a webserver to show the result of recommendtation.
##Architecture and explanations:

Book_Recommender/
├── BookRecommend.py
├── my_recommend.py
├── personalRatings.txt
├── prepare_data.py
├── rate_books.py
├── server.py
└── templates/
    ├── index.html
    ├── start.html
    └── rate.html

1. BooksRecommend.py: Import datasets, train model then save it.
   
2. my_recommend.py: Load trained model and personal ratings to get recommendtation.
   
3. personalRatings.txt: A text file to save personal ratings.
   
4. prepare_data.py: Pre-process datasets to adjust the model.
   
5. rate_books.py: Generate personalRatings.txt according to users's ratings.
   
6. server.py: Webserver to show the result of recommendation.
   
7. templates/: The folder to store html files, which are used to build the web page.
   

##Requirements:

1. pyspark
   
2. python2 or python3
   
3. flask
   
4. numpy
   

##Usage:

1. Set path of spark home. E.g.export SPARK_HOME=/path/to/spark/home
2. run webserver by python server.py
3. If the trained model is needed, you can run BooksRecommend.py in pyspark using the command subprocess.call(['/path/to/BookRecommend.py','/path/to/books.csv and ratings.csv','/path/to/personalRatigns.txt'])