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: 
  Next, let's collect some training data for the app of this Engine. By default,
  the Similar Engine Template supports 2 types of entities: **user** and
  **item** and event **view**. An item has the **categories** property, which is a list of category names (String). A user can view an item.
  
  You can send these data to PredictionIO Event Server in real-time easily by
  making a HTTP request or through the `EventClient` of an SDK.
  
negative.code: ---
  title: Quick Start - Similar Products Engine Template
  ---
  
  ## Overview
  This engine template recommends products that are "similar" to the input product(s).
  Similarity is not defined by text description but by users' previous actions. By default, it uses 'view' action such that product A and B are considered similar if most users who view A also view B.
  
  This template is ideal for recommending products to customers based on their recent actions.
  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 approach works perfectly for customers who are **first-time visitors** or have not signed in.
  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.
  
  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
  
  Input Query:
  
  - List of ItemIDs, which are the targeted products
  - N (number of items to be recommended)
  