Digging into UR algorithm

[Sami Serbey <sa...@designer-24.com>]

Hi guys,

So I've been playing around with the UR algorithm and I would like to know 2 things if it is possible:

1- Does UR recommend items that are linked to primary event only? Like if item A is pruchased (primary event) 1 time and item B is liked (secondary event) 50 times, does UR only recommend item A as the popular one even though item B have x50 secondary event? Is there a way to play around this?

2- When I first read about UR I thought that it recommend items based on the frequency of secondary events to primary events. ie: if 50 likes (secondary event) of item A lead to the purchase of item B and 1 view (secondary event) of item A lead to the purchase of item C, when someone view and like item A he will get recommended item B and C with equal score disregarding the 50 likes vs 1 view. Is that the correct behavior or am I missing something? Does all secondary event have same weight of influence for the recommender?

I hope that you can help me out understanding UR template.

Regards,
Sami Serbey

Re: Digging into UR algorithm

[Pat Ferrel <pa...@actionml.com>]

The CCO algorithm test for correlation with a statistic called the Log
Likelihood Ratio (LLR). This compares relative frequencies of 4 different
things 2 having to do with the entire dataset 2 having to do with the 2
events being compared for correlation. Popularity is normalized out of this
comparison but does play a small indirect part in having engough data to
make better guesses about correlation.

Also remember that the secondary event may have item-ids that are not part
of the primary event. For instance if you have good search data then one
(of several) secondary event might be (user-id, "searched-for”,
search-term) This as a secondary event has proven to be quite useful in at
least one dataset I’ve seen.


From: Pat Ferrel <pa...@occamsmachete.com> <pa...@occamsmachete.com>
Reply: Pat Ferrel <pa...@occamsmachete.com> <pa...@occamsmachete.com>
Date: July 2, 2018 at 12:18:16 PM
To: user@predictionio.apache.org <us...@predictionio.apache.org>
<us...@predictionio.apache.org>, Sami Serbey <sa...@designer-24.com>
<sa...@designer-24.com>
Cc: actionml-user <ac...@googlegroups.com>
<ac...@googlegroups.com>
Subject:  Re: Digging into UR algorithm

The only requirement is that someone performed the primary event on A and
the secondary event is correlated to that primary event.

the UR can recommend to a user who has only performed the secondary event
on B as long as that is in the model. Makes no difference what subset of
events the user has performed, recommendations will work event if the user
has no primary events.

So think of the model as being separate from the user history of events.
Recs are made from user history—whatever it is, but the model must have
some correlated data for each event type you want to use from a user’s
history and sometimes on infrequently seen items there is no model data for
some event types.

Popularity has very little to do with recommendations except for the fact
that you are more likely to have good correlated events. In fact we do
things to normalize/down weight highly popular things because otherwise
recommendations are worse. You can tell this by doing cross-validation
tests for popular vs collaborative filtering using the CCO algorithm behind
the UR.

If you want popular items you can make a query with no user-id and you will
get the most popular. Also if there are not enough recommendations for a
user’s history data we fill in with popular.

Your questions don’t quite match how the algorithm works so hopefully this
straightens out some things.

BTW community support for the UR is here:
https://groups.google.com/forum/#!forum/actionml-user


From: Sami Serbey <sa...@designer-24.com>
<sa...@designer-24.com>
Reply: user@predictionio.apache.org <us...@predictionio.apache.org>
<us...@predictionio.apache.org>
Date: July 2, 2018 at 9:32:01 AM
To: user@predictionio.apache.org <us...@predictionio.apache.org>
<us...@predictionio.apache.org>
Subject:  Digging into UR algorithm

Hi guys,

So I've been playing around with the UR algorithm and I would like to know
2 things if it is possible:

1- Does UR recommend items that are linked to primary event only? Like if
item A is pruchased (primary event) 1 time and item B is liked (secondary
event) 50 times, does UR only recommend item A as the popular one even
though item B have x50 secondary event? Is there a way to play around this?

2- When I first read about UR I thought that it recommend items based on
the frequency of secondary events to primary events. ie: if 50 likes
(secondary event) of item A lead to the purchase of item B and 1 view
(secondary event) of item A lead to the purchase of item C, when someone
view and like item A he will get recommended item B and C with equal score
disregarding the 50 likes vs 1 view. Is that the correct behavior or am I
missing something? Does all secondary event have same weight of influence
for the recommender?

I hope that you can help me out understanding UR template.

Regards,
Sami Serbey



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Re: Digging into UR algorithm

[Pat Ferrel <pa...@occamsmachete.com>]

The only requirement is that someone performed the primary event on A and
the secondary event is correlated to that primary event.

the UR can recommend to a user who has only performed the secondary event
on B as long as that is in the model. Makes no difference what subset of
events the user has performed, recommendations will work event if the user
has no primary events.

So think of the model as being separate from the user history of events.
Recs are made from user history—whatever it is, but the model must have
some correlated data for each event type you want to use from a user’s
history and sometimes on infrequently seen items there is no model data for
some event types.

Popularity has very little to do with recommendations except for the fact
that you are more likely to have good correlated events. In fact we do
things to normalize/down weight highly popular things because otherwise
recommendations are worse. You can tell this by doing cross-validation
tests for popular vs collaborative filtering using the CCO algorithm behind
the UR.

If you want popular items you can make a query with no user-id and you will
get the most popular. Also if there are not enough recommendations for a
user’s history data we fill in with popular.

Your questions don’t quite match how the algorithm works so hopefully this
straightens out some things.

BTW community support for the UR is here:
https://groups.google.com/forum/#!forum/actionml-user


From: Sami Serbey <sa...@designer-24.com>
<sa...@designer-24.com>
Reply: user@predictionio.apache.org <us...@predictionio.apache.org>
<us...@predictionio.apache.org>
Date: July 2, 2018 at 9:32:01 AM
To: user@predictionio.apache.org <us...@predictionio.apache.org>
<us...@predictionio.apache.org>
Subject:  Digging into UR algorithm

Hi guys,

So I've been playing around with the UR algorithm and I would like to know
2 things if it is possible:

1- Does UR recommend items that are linked to primary event only? Like if
item A is pruchased (primary event) 1 time and item B is liked (secondary
event) 50 times, does UR only recommend item A as the popular one even
though item B have x50 secondary event? Is there a way to play around this?

2- When I first read about UR I thought that it recommend items based on
the frequency of secondary events to primary events. ie: if 50 likes
(secondary event) of item A lead to the purchase of item B and 1 view
(secondary event) of item A lead to the purchase of item C, when someone
view and like item A he will get recommended item B and C with equal score
disregarding the 50 likes vs 1 view. Is that the correct behavior or am I
missing something? Does all secondary event have same weight of influence
for the recommender?

I hope that you can help me out understanding UR template.

Regards,
Sami Serbey