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: Hi, 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. /magnus 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 Hi, 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. Regards 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."