*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 README.md 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.registrator":
"org.apache.mahout.sparkbindings.io.MahoutKryoRegistrator",
"spark.kryo.referenceTracking": "false",
"spark.kryoserializer.buffer.mb": "300",
"spark.kryoserializer.buffer": "300m",
"es.index.auto.create": "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"
}]
}
}
]
}
