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Correlated Cross-Occurrence (CCO): How to make data behave



Cross-occurrence allows us to ask the question: are 2 events correlated.

 To use the Ecom example, purchase is the conversion or primary action, a detail page view might be related but we must test each cross-occurrence to make sure. I know for a fact that with many ecom datasets it is impossible to treat these events as the same thing and get anything but a drop in quality of recommendations (I’ve tested this). People that use the ALS recommender in Spark’s MLlib sometimes tell you to weight the view less than the purchase. But this is nonsense (again I’ve tested this). What is true is that *some* views lead to purchases and others do not. So treating them all with the same weight is pure garbage.

 What CCO does is find the views that seem to lead to purchase. It can also find category-preferences that lead to certain purchases, as well as location-preference (triggered by a purchase when logged in from some location).  And so on. Just about anything you know about users or can phrase as a possible indicator of user taste can be used to get lift in quality of recommendation.

 So in the example below purchase history is the conversion action, likes, and downloads are secondary actions looked at as cross-occurrences. Note that we don’t need to have the same IDs for all actions. 

 BTW to illustrate how powerful this idea is, I have a client that sells one item a year on average to a customer. It’s a very big item and has a lifetime of one year. So using ALS you could only train on the purchase and if you were gathering a year of data there would be precious little training data. Also when you have a user with no purchase it is impossible to recommend. ALS fails on all users with no purchase history. However with CCO, all the user journey and any data about the user you can gather along the way can be used to recommend something to purchase. So this client would be able to recommend to only 20% of their returning shoppers with ALS and those recs would be low of quality based on only one event far in the past. CCO using all the clickstream (or important parts of it) can do quite well.

 This may seem an edge case but only in degree, every ecom app has data they are throwing away and CCO addresses this.
A way to compare 2 events at the individual level. The comparison is called the Log-Likelihood Ratio or LLR. This would allow us to look across all page views and see which correlated with which purchases. , not all page view are created equal but we now had a way to find the important ones!

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