anchor.diff:  {"itemScores":[{"item":"i12","score":1.1700499715209998},{"item":"i21","score":1.1153550716504106},{"item":"i43","score":1.1153550716504106},{"item":"i14","score":1.0773502691896257},{"item":"i39","score":1.0773502691896257},{"item":"i26","score":1.0773502691896257},{"item":"i44","score":1.0773502691896257},{"item":"i38","score":0.9553418012614798},{"item":"i36","score":0.9106836025229592},{"item":"i46","score":0.9106836025229592}]}
   ```
   
  -In addition, the Query support the following optional parmaters `categories`, `whiteList` and `blackList`.
  -
  -Recommend items with selected categories:
  +In addition, the Query support the following optional parameters `categories`, `whiteList` and `blackList`.
  +
  +### Recommend items in selected categories:
   
   ```
   curl -H "Content-Type: application/json" \
  
positive.code: 160 events are imported.
  ```
  
  WARNING: If you experience error similar to the following:
  **TypeError: __init__() got an unexpected keyword argument 'access_key'**
  please update the Python SDK to the latest version.
  
  ## Deploy the Engine as a Service
  
negative.code: - List of white-listed ItemIds (optional)
  - List of black-listed ItemIds (optional)
  
  The template also supports black-list and white-list. If a white-list is provided, the engine will include only those products in the recommendation.
  Likewise, if a black-list is provided, the engine will exclude those products in the recommendation.
  
  Output PredictedResult:
  
  - a ranked list of recommended itemIDs
  
  ### Predictive Model Training
  
  By default, this template takes the following data from Event Server as Training Data:
  
  - Users' *view* events
  - Items' *categories* properties
  
  
  
  INFO: This template can easily be customized to consider more user events such as *buy*, *rate* and *like*.
  You can offer features like "Customers Who Bought This Item Also Bought....".
  
  
  ## Create a new Engine from an Engine Template
  
  Now let's create a new engine called *MySimilar* by copying the Similar Product Discovery Engine Template, assuming `/home/yourname/predictionio`
  is the installation directory of PredictionIO:
  
  ```
  