anchor.diff:  ### Recommend items in selected categories:
   
   ```
  -curl -H "Content-Type: application/json" \
  +$ curl -H "Content-Type: application/json" \
   -d '{
     "user": "u1",
  -  "num": 10,
  +  "num": 4,
     "categories" : ["c4", "c3"]
   }' \
   http://localhost:8000/queries.json
   
  -{"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}]}
  +{"itemScores":[{"item":"i4","score":0.006009267718658978},{"item":"i33","score":0.005999267822052033},{"item":"i14","score":0.005261309429391667},{"item":"i2","score":0.002848917309942939}]}
   ```
   
   ### Recommend items in the whiteList:
   
   ```
  -curl -H "Content-Type: application/json" \
  +$ curl -H "Content-Type: application/json" \
   -d '{
     "user": "u1",
  -  "num": 10,
  -  "categories" : ["c4", "c3"],
  -  "whiteList": ["i21", "i26", "i40"]
  +  "num": 4,
  +  "whiteList": ["i1", "i2", "i3", "i21", "i22", "i23", "i24", "i25"]
   }' \
   http://localhost:8000/queries.json
   
  -{"itemScores":[{"item":"i21","score":1.1153550716504106},{"item":"i26","score":1.0773502691896257}]}
  +{"itemScores":[{"item":"i3","score":0.003007015026561692},{"item":"i2","score":0.002848917309942939},{"item":"i23","score":0.0016857619403278443},{"item":"i25","score":1.3707548965227745E-4}]}
   ```
   
   ### Recommend items not in the blackList:
  
positive.code: -d '{ "user": "u1", "num": 4 }' \
  http://localhost:8000/queries.json
  
  {"itemScores":[{"item":"i43","score":0.7071067811865475},{"item":"i21","score":0.7071067811865475},{"item":"i46","score":0.5773502691896258},{"item":"i8","score":0.5773502691896258}]}
  ```
    </div>
  </div>
  
negative.code: 
  ## Using the Engine
  
  Now, You can retrieve predicted results. To recommend 4 items to user ID "u1". You send this JSON `{ "user": ["u1"], "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">
  