BTW h_a(AtA) is called cooccurrence and has been widely documented and 
experimented with. The primary extension we make is cross-occurrence to add new 
events. Cross validation tests for predictive value of recommendations and A/B 
tests have been done many times. Mostly these are private so the results are 
hard to share but I am unaware of anyone doing these that did not stick with 
the UR. One experiment put the UR up against a big name SaaS recommender 
company and the UR beat them by a very large margin. The SaaS company had their 
people tune their algorithm and the UR was not tuned—just using defaults. The 
UR won this test by about 300%. I hasten to add that this seems a ridiculous 
margin, much more than most cases could hope for and if the SaaS company hadn’t 
set it up themselves I’d think there was some major error on their side.

The best method is to choose a candidate or 2 then find a winner or validate 
your choice with A/B tests, don’t decide on the ability to “tune” alone. The 
SaaS company may have actually de-tuned to get such abysmal results. They don’t 
even answer questions about how they tune (or de-tune).

In any case it is free OSS, feel free to use anything you want or modify the 
source to tune anything your intuition says might work.

On Dec 1, 2016, at 11:48 AM, Pat Ferrel <> wrote:

This is a very odd statement. How many tuning knobs do you have with MLlib’s 
ALS, 1, 2? There are a large number of tuning knobs for the UR to fit different 
situations. What other recommender allows multiple events as input? The UR also 
has business rules in the form of filters and boosts on item properties. I 
think you may have missed a lot in the docs, check some of the most important 
tuning here: 
<> and the config params for 
business rule here: 

But changes must be based on either A/B tests or cross-validation. Guessing at 
tuning is dangerous, intuition about how a big-data algorithm works takes a 
long time to develop and the trade-offs may do your business harm.

We have a tool that find optimal LLR thresholds based on predictive strength 
and sets the threshold per event pair. While you can set these by hand the 
pattern we follow is called hyper-parameter search, which finds optimal tuning 
for you. 

On Dec 1, 2016, at 11:17 AM, Harsh Mathur < 
<>> wrote:

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 wrong.

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 returned.

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" < 
<>> 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 < 
<>> 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 < 
Sent: Monday, November 28, 2016 3:46:46 PM
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 <>

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

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