Hi Pat,
I really appreciate the product, but our team was discussing about how
little control we have here.
As in, say some recommendations got delivered to the user and we are
tracking conversions of course, so we can know if it's working or not. Now,
say if we see that conversions are low, as a developer I have very little
to experiment with here. I don't mean any disrespect. I have gone through
the code and have put in efforts to understand it too,  the UR is still
better than the explicit or implicit templates as it has filtration for
properties, only thing lacking in my opinion is the weightages.

I read your ppt
Recommendations = PtP +PtV+...
We were wondering if it could be
Recommendations = a * PtP + b * PtV+ ...

Where a and b are constants for tuning. In my understanding PtP is a matrix
so scalar multiplication should have be possible. Please correct me if I am

Also I was reading about log likelihood method, but I couldn't find a
proper explanation. I would be happy if anyone here can explain it in more
detail. Thanks in advance.

Here is what I understood.
For every item-item pair per expression (PtP, PtV), to calculate a score,
it will find 4 things:
1. No of users who posted both events for the pair,
2. No of users who posted event for one but not the other and vice versa,
3. No of users who posted for neither

Then a formula is applied taking the 4 params as input and a score is

For each item and event pair you are storing top 20 items according to
score in elastic search. I didn't understand why the 2nd and third
parameters are taken, also if anyone can explain the correctness of the
method, That is why does it work rather how it works?

Harsh Mathur
On Dec 1, 2016 11:01 PM, "Pat Ferrel" <p...@occamsmachete.com> wrote:

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

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