anchor.diff:  ---
  -title: Quick Start - Similar Product Discovey Engine Template
  +title: Quick Start - Similar Products 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.
  +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 kind of recommendation works perfectly even for customers who have not signed in and  first-time vistors.
  +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.
   
  -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.
  +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):
  +## Usage
  +
  +Input Query:
   
   - List of ItemIDs, which are the targeted products
   - N (number of items to be recommended)
  
positive.code: 
  ### Training the Predictive Model
  
  Train your successfully-built engine.
  
  ```
  $ pio train
  
negative.code: {"itemScores":[{"item":"i21","score":1.1153550716504106},{"item":"i14","score":1.0773502691896257},{"item":"i26","score":1.0773502691896257},{"item":"i39","score":1.0773502691896257},{"item":"i44","score":1.0773502691896257},{"item":"i45","score":0.7886751345948129},{"item":"i47","score":0.7618016810571367},{"item":"i9","score":0.7618016810571367},{"item":"i28","score":0.7618016810571367},{"item":"i6","score":0.7618016810571367}]}
  ```
  
  ### Recommend items in the whiteList:
  
  ```
  curl -H "Content-Type: application/json" \
  