Recommendation Engine in Docker Container!

Recommendation Engine in Docker Container!

Check out my docker container with recommendation engine serving recommendations via PubNub queues!

You can find introduction to the idea of Subscribe-Serve and Subscribe-Get Service in my previous post Recommendation as a Microservice.

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How to run your own recommendation micro-service? It's very easy. You can do it in just few simple steps.

Steps Summary

1. Pull docker image.
2. Start docker container.
3. Train.
4. Get service.

Detailed Instructions

1. Pull docker image.

docker pull goliasz/raas-micro:1.1

2. Create your account and first queue in PubNub.

• Go to PubNub home.
• Register. The simples way is just by using Google account.
• Create you PubNub App.

PubNub application with publish and subscribe keys assigned

Once you have your PubNub application you have your Publish Key and Subscribe Key assigned. 

• Start Debug Console

Debug console before adding clients

• Choose your channel name
• Add two clients

Two queue clients added. First maximized.

3. Start you docker container using your Subscribe Key, Publish Key and Channel ID.

docker run -dt --hostname reco1 --name reco1 -e "PN_PUBKEY=pub-c-1113-demo-3" -e "PN_SUBKEY=sub-c-1f1a-demo" -e "PN_CHANNEL=Channel-mydemo-154" goliasz/raas-micro:1.1 /MyEngine/autostart.sh

Wait two minutes and you should see in your PubNub queue readiness messages.

Readiness messages

You should see three messages.

{
  "msg": "training ready",
  "rtype": "info"
}

{
  "msg": "query ready",
  "rtype": "info"
}

{
  "msg": "service ready",
  "rtype": "info"
} 

4. Train your recommender engine with some data.

Copy/Paste one by one training messages below to PubBub client window and "Send" after each message.

{

  "event": "purchase",

  "entityType": "user",

  "entityId": "u1",

  "targetEntityType": "item",

  "targetEntityId": "Iphone 6",

  "rtype": "train"

}

Click "Send"

{

  "event": "view",

  "entityType": "user",

  "entityId": "U 2",

  "targetEntityType": "item",

  "targetEntityId": "Phones",

  "rtype": "train"

}

Click "Send"

{

  "event": "$set",

  "entityType": "item",

  "entityId": "Galaxy",

  "properties": {

        "categories": [

          "Phones",

          "Electronics",

          "Samsung"

        ]

  },

  "rtype": "train"

}

Click "Send"

After sending first message you should see the message repeated in second client window.

Training message repeated in second client window.

 

After sending all training messages

Now you have to instruct the engine to train its recommendation model. Send service message below.

{

  "cmd": "retrain",

  "rtype": "service"

}

Service "Retrain" message sent

Wait 3 or 4 minutes and get your recommendation.

5. Ask for recommendations

Just send query message.

{

  "user": "u1",

  "item": "Iphone 5",

  "num": 5,

  "rtype": "query"

}

Query message and response with recommendation

You should receive message with recommendation.

Example:

{
  "itemScores": [
    {
      "item": "Galaxy",
      "score": 1.3233743906021118
    }
  ],
  "rtype": "response"
}

Congratulations! You have your own recommender engine in Subscribe-Server architecture!

Conclusions

• Nothing stops you to have your own recommender engine.
• It is easy!

Do you have any problems? Just call me. Contact details here http://kolibero.eu/contact

 

Resources

• https://hub.docker.com/r/goliasz/raas-micro/ 
• http://kolibero.eu
• http://recomme.eu/raas
• http://recomme.eu/raas/raas.html
• https://prediction.io
• http://blog.prediction.io/universal-recommender/
• http://occamsmachete.com/ml/

 (c) KOLIBERO, 2016

Recommendation as a Microservice

Recommendation as a Microservice

Successful e-business is using recommendations and this has become a standard. Good recommendations can increase conversion rate by significant percentage. In such cases recommendation engines are worth money spent. But they don't need to be expensive.

Low cost recommendations for everybody

Development state of machine learning platforms is significant and situation is mature enough to make recommendation just as a simple and cheap service.

How about exposing recommendation engines via secure queues available to every business and organization. Such queues are already available. Great example of such service are PubNub queues. This is great simple and secure technology designed for new Internet of Things era.

Subscribe - Get Service 

Subscribe - Get Service

How about just subscribing to a secure queue and receiving recommendation in 200 ms? Secure PubNub queues are accessible right now. You can have your own queue in just couple of clicks. What if on the other side of the queue is micro-service delivering recommendations on demand. Your personal instance of recommendation engine. Technology is available right now.

See my recommendation engine demo serving recommendations via queue.

Subscribe - Train

To have recommendations you need to train your engine with data related to your business. How to do it? Again secure queue is a solution. Just subscribe to your queue and publish fully anonymous data (just ids) to your personal instance of recommendation engine.

Training of Recommendation Engine.

Subscribe - Serve

Trained machine learning engine is just subscribing back to your queue and serving personalized recommendations tailored only to your needs.

Subscribe - Serve Recommendations

The engine can be deployed anywhere. In-house or in the cloud like Amazon's AWS. It can be just rented for some time or just for a finite number of recommendations. Deployment models are fully flexible. Just imagine your solution!

Endless Possibilities

And now! What if we need another service? If we want to answer another question? Some examples. How long is my customer going to stay with me? When is the customer going to buy the product again? What to do to make him/her stay?

Conclusions 

• Good recommendation is not a luxury. It is available at hand. Just great service at low cost.
• You don't need data scientists or big IT departments to have it.
• You can integrate it with your platforms in super easy and secure way.

Resources

• https://hub.docker.com/r/goliasz/raas-micro/
• http://recomme.eu/raas
• https://prediction.io
• http://blog.prediction.io/universal-recommender/