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From Dennis Honders <>
Subject UR optimizing results
Date Wed, 24 May 2017 15:28:54 GMT
*Current data: *

{"event": "cart-transaction", "entityId": "1", "entityType": "user",
"targetEntityId": "12", "targetEntityType": "item"},

{"event": "$set", "entityType": "item", "entityId": "12", "properties":
{"category": ["1", "2", "3", "4", "5", "6", "7"], "manufacturer": 1,
"label": "test", "price": "$1-$2"}}

*Questions: *

Cart-transaction is the primary for shopping cart recommendation, maybe use
user-buy-item as secondary event or is there no link between this?

Item-based queries are for similar items. For shopping cart
recommendations, complementary recommendations will suite better? If so,
those are made by 'user-id' (cart-id). How can this be done?

I like to do content-based recommendation for items that haven't been in a
transaction. I think this can be configured in the engine.json. Any advice
for doing this?

*Engine.json: *

  "comment":" This config file uses default settings for all but the
required values see for docs",
  "id": "default",
  "description": "Default settings",
  "engineFactory": "com.actionml.RecommendationEngine",
  "datasource": {
    "params" : {
      "name": "ur-name",
      "appName": "Test",
      "eventNames": ["cart-transaction"]
  "sparkConf": {
    "spark.serializer": "org.apache.spark.serializer.KryoSerializer",
    "spark.kryo.referenceTracking": "false",
    "spark.kryoserializer.buffer.mb": "300",
    "spark.kryoserializer.buffer": "300m",
    "": "true"
  "algorithms": [
      "comment": "simplest setup where all values are default, popularity
based backfill, must add eventsNames",
      "name": "ur",
      "params": {
"appName": "Test",
"indexName": "test",
"typeName": "cart",
"comment": "must have data for the first event or the model will not build,
other events are optional",
"eventNames": ["cart-transaction"],
"maxEventsPerEventType": 50000,
"maxCorrelatorsPerEventType": 5000,
"num": 10,
"itemBias": 2.0,
"rankings": [{
"name": "preferredRank",
"type": "userDefined"

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