Exactly so. The weighting of events is done by the algorithm. To add biases 
would very likely be wrong and result in worse results. It is therefore not 
supported in the current code. There may be a place for this type of bias but 
it would have to be done in conjunction with a cross-validation tests we have 
in our MAP test suite and it is not yet supported. Best to leave them with the 
default weighting in the CCO algorithm, which is based on the strength of 
correlation with the conversion event, which I guess is purchase in your case.

On Nov 28, 2016, at 2:19 PM, Magnus Kragelund <m...@ida.dk> wrote:

It's my understanding that you cannot apply a bias to the event, such as "view" 
or "purchase" at query time. How the engine is using your different events to 
calculate score, is something that is in part defined by you and in part 
defined during training.
In the engine.json config file you set an array of event names. The first event 
in the array is considered a primary event, and will be the event that the 
engine is trying to predict. The other events that you might specify is 
secondary events, that the engine is allowed to take in to consideration, when 
finding correlations to the primary event in your data set. If no correlation 
is found for a given event, the event data is not taken into account when 
predicting results. 

Your array might look like this, when predicting purchases: ["purchase",  
"initiated_payment", "view", "preview"]

If you use the special $set event to add metadata to your items, you can apply 
a bias or filter on those metadata properties at query time.


From: Harsh Mathur <harshmathur.1...@gmail.com>
Sent: Monday, November 28, 2016 3:46:46 PM
To: user@predictionio.incubator.apache.org
Subject: Tuning of Recommendation Engine
I have successfully deployed the UR template.

Now I wanted to tune it a little bit, As of now I am sending 4 events, 
purchase, view, initiated_payment and preview. Also our products have 
categories, I am setting that as item properties.

Now, as I query say:
"item": "{item_id}",
"fields": [
"name": "view",
"bias": 0.5
"name": "preview",
"bias": 5
"name": "purchase",
"bias": 20

and query 
        "item": "{item_id}"

For both queries, I get the same number of recommendations just the score 
varies. The boosting isn't changing any recommendations, just changing the 
scores. Is there any way in UR that we can give more preference to some events, 
it will help give us more room to try and see and make the recommendations more 
relevant to us.

Harsh Mathur
harshmathur.1...@gmail.com <mailto:harshmathur.1...@gmail.com>

“Perseverance is the hard work you do after you get tired of doing the hard 
work you already did."

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