This piece of work is to showcase content based (item-item) recommendation of movies. To do this first top 250 imdb movies are crawled and stored as json leveraging BeautifulSoup package. Then a cosine similarity based recommendation system is built using functionalities of pandas, numpy and scikit-learn.

1. create aws access key and secret key
2. Create an S3 bucket.
3. Set the envoronment variables in docker-compose-recsys.yml and docker-compose-aws.yml, from None value to appropriate values.

1. git clone https://github.com/tuhinsharma/imdb-rec-sys.git
2. cd imdb-rec-sys
3. Follow COMMON_STEP
4. Use docker-compose
   1. docker-compose -f docker-compose-recsys.yml build
   2. docker-compose -f docker-compose-recsys.yml up
5. For Crawling : curl -H 'Content-Type: application/json' -X POST -d {} http://localhost:6006/api/v1/schemas/crawl_imdb
6. For Training : curl -H 'Content-Type: application/json' -X POST -d {} http:imdb//localhost:6006/api/v1/schemas/train
7. For Recommendation : curl -H 'Content-Type: application/json' -X POST -d '{"movie_list": ["The Green Mile","Witness for the Prosecution"]}' http://localhost:6006/api/v1/schemas/score

1. Choose EC2 instance Ubuntu 16.04 LTS - Xenial (HVM)
2. Configure security group - SSH - custom, HTTP - anywhere
3. Launch instance using key-value pair - tuhin-aws
4. ssh into EC2 machine - ssh -i "tuhin-aws.pem" ubuntu@ec2-54-234-224-219.compute-1.amazonaws.com
5. sudo apt update --fix-missing
6. sudo apt install -y python3-pip
7. sudo apt install -y nginx
8. open nginx.conf file → sudo vi /etc/nginx/nginx.conf → change user ubuntu; and add server_names_hash_bucket_size 128; in the http block
9. open virtual.conf file → sudo vi /etc/nginx/conf.d/virtual.conf → add

server {
    listen       80;
    server_name  ec2-54-234-224-219.compute-1.amazonaws.com;
    location / {
        proxy_pass http://127.0.0.1:8000;
    }
}

10. sudo systemctl start nginx
11. git clone https://github.com/tuhinsharma/imdb-rec-sys.git
12. cd imdb-rec-sys
13. Follow COMMON_STEP
14. sudo pip3 install -r requirements.txt
15. cp ./rec_platform/deployment/app.py ./app.py
16. sudo systemctl restart nginx
17. gunicorn --pythonpath / -b localhost:8000 -k gevent -t 900 app:app -w 5 &

• In local system:- curl -H 'Content-Type: application/json' -X POST -d '{"movie_list": ["The Green Mile","Witness for the Prosecution"]}' http://ec2-54-234-224-219.compute-1.amazonaws.com/api/v1/schemas/score The output should be:-

{
  "movies": [
    "L.A. Confidential", 
    "Salinui chueok", 
    "Les diaboliques", 
    "12 Angry Men", 
    "Double Indemnity", 
    "Chinatown", 
    "On the Waterfront", 
    "A Wednesday", 
    "Se7en", 
    "The Usual Suspects"
  ]
}

• In remote system do pkill gunicorn and sudo systemctl stop nginxif service no longer needed.

1. Choose EC2 instance ubuntu 16.04 LTS - Xenial (HVM)
2. Configure security group - SSH - custom, HTTP - anywhere
3. Launch instance using key-value pair - tuhin-aws
4. ssh into EC2 machine - ssh -i "tuhin-aws.pem" ubuntu@ec2-54-234-224-219.compute-1.amazonaws.com
5. sudo apt update --fix-missing
6. sudo apt install -y docker.io
7. sudo apt install -y docker-compose
8. git clone https://github.com/tuhinsharma/imdb-rec-sys.git
9. cd imdb-rec-sys
10. Update the docker-compose-recsys.yml with suitable ACCESS_KEY and SECRET_ACCESS_KEY and AWS_BUCKET_NAME. Port mapping should be "80:6006"
11. sudo docker-compose -f docker-compose-recsys.yml build
12. sudo docker-compose -f docker-compose-recsys.yml up

• In local system:- curl -H 'Content-Type: application/json' -X POST -d '{"movie_list": ["The Green Mile","Witness for the Prosecution"]}' http://ec2-54-234-224-219.compute-1.amazonaws.com/api/v1/schemas/score The output should be:-

{
  "movies": [
    "L.A. Confidential", 
    "Salinui chueok", 
    "Les diaboliques", 
    "12 Angry Men", 
    "Double Indemnity", 
    "Chinatown", 
    "On the Waterfront", 
    "A Wednesday", 
    "Se7en", 
    "The Usual Suspects"
  ]
}

1. configure aws with ACCESS_KEY and SECRET_ACCESS_KEY
2. git clone https://github.com/tuhinsharma/imdb-rec-sys.git
3. cd imdb-rec-sys
4. aws ecr create-repository --repository-name recsys-ubuntu
5. $(aws ecr get-login --no-include-email --region us-east-1)
6. docker build -t recsys-ubuntu -f Dockerfile.ubuntu .
7. docker tag recsys-ubuntu:latest 184213940252.dkr.ecr.us-east-1.amazonaws.com/recsys-ubuntu:latest
8. docker push 184213940252.dkr.ecr.us-east-1.amazonaws.com/recsys-ubuntu:latest
9. Update the docker-compose-aws.yml with suitable ACCESS_KEY and SECRET_ACCESS_KEY and AWS_BUCKET_NAME. Port mapping should be "80:6006". image should be 184213940252.dkr.ecr.us-east-1.amazonaws.com/recsys-ubuntu
10. ecs-cli configure --region us-east-1 --cluster fastfilmz-analytics-cluster
11. ecs-cli up --keypair tuhin-aws --capability-iam --size 1 --instance-type t2.micro --force --cluster fastfilmz-analytics-cluster --region us-east-1
12. ecs-cli compose --project-name imdb-recsys --file docker-compose-aws.yml up
13. In case Outdated ECS Agent - aws ecs update-container-agent --cluster fastfilmz-analytics-cluster --container-instance bc7e2a68-1be6-48d2-85a6-7f08232f298b

• In local system:- curl -H 'Content-Type: application/json' -X POST -d '{"movie_list": ["The Green Mile","Witness for the Prosecution"]}' http://ec2-54-234-224-219.compute-1.amazonaws.com/api/v1/schemas/score The output should be:-

{
  "movies": [
    "L.A. Confidential", 
    "Salinui chueok", 
    "Les diaboliques", 
    "12 Angry Men", 
    "Double Indemnity", 
    "Chinatown", 
    "On the Waterfront", 
    "A Wednesday", 
    "Se7en", 
    "The Usual Suspects"
  ]
}

14. If done with the service ecs-cli down