anchor.diff:  - List of white-listed ItemIds (optional)
   - List of black-listed ItemIds (optional)
   
  -If a white-list is provided, the engine will include only those products in the recommendation.
  +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.
   
  -PredictedResult (Output):
  +Output PredictedResult:
   
   - a ranked list of recommended itemIDs
   
   ### Predictive Model Training
   
  -This template takes the following data from Event Server as Training Data:
  +By default, this template takes the following data from Event Server as Training Data:
   
   - Users' *view* events
   - Items' *categories* properties
   
   
  -### Extendibility
  -
  -1. Consider more events
  -This template can easily be customized to consider more user events such as *buy*, *rate* and *like*.
  +
  +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....".
   
   
  -## Quick Start - Step-by-Step
  -
   ## Create a new Engine from an Engine Template
   
  -Now let's create a new engine called *MySimilar* by cloning the Similar Product Discovery Engine Template, assuming `/home/yourname/predictionio`
  +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:
   
   ```
  
positive.code: ---
  title: Quick Start - Similar Product Discovey Engine Template
  ---
  
  ## Overview
  This engine template predicts (or recommends) products that are "similar" to the targeted product(s).
  Similarity is not defined by text description but by users' previous "view" action.
  For example, product A and B are considered similar if most users who view A also view B.
  
  ### Use Cases
  
  1. Product Recommendation - For both *first-time* visitors and *existing* customers
  
  This template is ideal for recommending products to customers based on their recent actions on a website or mobile app.
  Use the IDs of the recently viewed products of a customer as the *Query*, 
  the engine will predict other products that this customer may also like.
  
  This kind of recommendation works perfectly even for customers who have not signed in and  first-time vistors.
  Recommendations are made dynamically in *real-time* based on the most recently product preference you provide in the *Query*. 
  You can, therefore, recommend products to visitors without knowing a long history about them.
  
  2. People who view A also view X, Y, Z
  
  You can also use this template to build the popular feature of Amazon: "Customers Who Viewed This Item Also Viewed..." quickly.
  Help your customers explore more products that they like, sell more products.
  
  ### Usage
  
  Query (Input):
  
  - List of ItemIDs, which are the targeted products
  - N (number of items to be recommended)
  
negative.code: 
  ![Engine Status](/images/engine-server.png)
  
  ## Using the Engine
  
  Now, You can retrieve predicted results. To retrieve 4 items which are similar to item ID "i1". You send this JSON `{ "items": ["i1"], "num": 4 }` to the deployed engine and it will return a JSON of the recommended items. Simply send a query by making a HTTP request or through the `EngineClient` of an SDK:
  
  <div class="tabs">
    <div data-tab="Python SDK" data-lang="python">
  