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Generic Ecommerce Recommendation Engine Template

Documentation

Steps to run

Step 1: Create the gradle wrapper to get a stable version of gradle.
$ pio app new ecommerce01
# if ecommerce01 already exists,clean data first
$ pio app data-delete ecommerce01
Step 2: Start PredictionIO Event Server.
$ pio eventserver
Step 3: Import More Sample Data.

Install Python SDK

$ pip install predictionio
# or
$ easy_install predictionio

Replace the value of access_key parameter by your Access Key and run

$ python data/loan.py --file data/loan_sample.csv --access_key $ACCESS_KEY

Find your access_key

$  pio app list

You should see something like this:

[INFO] [App$]                 Name |   ID |                                                       Access Key | Allowed Event(s)
[INFO] [App$]          ecommerce01 |    2 | Jhe05JGGEFzWHZpDDIkCs1lKyujGMXg4GXBa7i0QPMh2oqmW8G0RcbszZkMNHbjG | (all)
[INFO] [App$]              tapster |    1 | Dw5lyfoJxdEz2N1lQ9xskPDn5G40xaVNyVLUwtza9IbsYp0shAc2CMHWWqh5yb3a | (all)
[INFO] [App$] Finished listing 2 app(s).
Step 4: Building
$ pio build --verbose
Step 5: Training the Predictive Model
$ pio train
Step 6: Deploying the Engine
$ pio deploy

Versions

v0.1.0

  • Create the basic engine template with the purpose of:

    • Provide generic recommendation capability on top of machine learning algorithms
    • Generalize scenarios where users buy products and write reviews

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