Re: Digging into UR algorithm

[Pat Ferrel]

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 <sami....@designer-24.com>
Reply: us...@predictionio.apache.org <us...@predictionio.apache.org>
Date: July 2, 2018 at 9:32:01 AM
To: 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

[Pat Ferrel]

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.

--
You received this message because you are subscribed to the Google Groups "actionml-user" group.
To unsubscribe from this group and stop receiving emails from it, send an email to actionml-use...@googlegroups.com.
To post to this group, send email to action...@googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/actionml-user/CAOtZQD8CU5fVvZ9C32Cj6YaC1F%2B7oxWF%2Br21ApKnuajOZOFuoA%40mail.gmail.com.
For more options, visit https://groups.google.com/d/optout.