Monday Apr 16, 2012

Waterfall Predictions

Facet Based Predictions are a powerful method to increase predictive accuracy and facilitate rapid learning and knowledge transfer, but the simple approach described in an earlier post comes at a price. By using a single facet rather than individual choices for prediction, we decrease the granularity of our predictions. Choices that share the same facet value will be treated as equals by our predictive model; and even when important distinctions could be made after sufficient feedback is collected our simple facet based model will never learn to exploit these differences.

In most cases, the advantages of facet based models will outweigh the drawback of a reduction in granularity. This is especially true in implementations where shelf-life is short and no individual choice is ever expected to gather enough responses to build a predictive model. However, sometimes we will want to combine the power of facet based prediction with the accuracy of models defined at the lowest level of granularity; for instance when some choices are expected to collect sufficient feedback while others are not.

The complete and open decision management framework architecture of Oracle Real-Time Decisions allows us to blend predictive models in several ways. In this post, we will describe how we can mix-and-match two models at different levels detail using an approach we call waterfall prediction.

Waterfall Prediction

In essence, the waterfall method described here will try to predict a likelihood at the lowest possible level of granularity. If the choice based model at this grade has not received enough feedback to be considered mature, we will resort to a facet based model.

This implementation will build on the example described in our previous post about facet based prediction.

Product Model Setup

In addition to the facet based category events model configured earlier we will need a choice based event model Product Events. This model will predict likelihoods of events (Accepted and Ordered) based on feedback for individual choices.

Product Model

Recording Events

Previously, we would record feedback events only for our facet based model. As we now have two models at different levels of granularity, we will alter our code slightly to ensure we record any event against both models.

// create a new choice to represent the product
ProductsChoice p = Products.getChoice(request.getChoice());
// create a new choice to represent the category attribute
CategoriesChoice c = new CategoriesChoice(Categories.getPrototype());

// set properties of the category choice
c.setSDOId("Categories$" + p.getCategory());

// record choice in models (catching an exception just in case)
try { p.recordEvent(request.getEvent()); } catch (Exception e) { logError(e); }
try { c.recordEvent(request.getEvent()); } catch (Exception e) { logError(e); }

Note that we are recording the same event twice, but against two separate choices p and c representing the two different levels of granularity. The Oracle Real-Time Decisions framework will automatically ensure the relevant models are updated accordingly.

Predicting Likelihoods

In this implementation, a new function will be used to predict likelihoods for our products. Rather than just returning the likelihood at the category level like before, this function will first check whether the more granular model has received enough feedback (in this case 100 events) to be considered mature. If the product model is deemed sufficiently trained, the function will use this model instead of the more general facet base one.

// get instance of the model used for predicting Product Events
ProductEvents productmodel = ProductEvents.getInstance();
// get instance of the model used for predicting Category Events
CategoryEvents categorymodel = CategoryEvents.getInstance();

// check if model for Product Events is sufficiently trained
if (productmodel.getChoiceEventModelCount(ModelCount.POSITIVE_COUNT, 
	product.getSDOId(), event) >= 100)
{
    // return the likelihood based on the Product Event model
    return productmodel.getChoiceEventLikelihood("Products$"+product.getSDOId(), event);        
}
// else if the model is not sufficiently trained
else {  
    // return the likelihood based on the Category Event model
    return categorymodel.getChoiceEventLikelihood("Categories$"+product.getCategory(), event);
}

Waterfall Function 

Proper decision design and continuous in-live testing are crucial here. Precisely how much feedback should be considered “enough feedback” can wildly differ between implementations and use-cases. Moreover, some implementation might also permit the use of the quality of the models as reported by RTD, rather than the number of positive events, to determine the cascade threshold. Decision Center is an indispensable tool in this process.

Choice Group Scores Setup

Similar to before, on the scores tab for the Products choice group we configure the Likelihood performance goal to be populated by the new WaterfallLikelihood function instead of the PredictLikelihood function.

Waterfall Score

These simple changes to our previous example empower our new implementation to benefit from two models at varying levels of granularity; leveraging both the accuracy of choice based models and the advantages of facet based prediction.

The Power of Waterfall Prediction

Waterfall prediction is a compelling example of how Oracle Real-Time Decisions enables us to blend multiple real-time models for use in rapid decisioning. This advanced approach to modeling can easily be expanded to cover more than just two levels and more than one hierarchy to further improve the predictive prowess of an RTD implementation.

Cascading models allows businesses to express various forms of decision logic that are far more subtle than simple one-step prediction models. In implementations where convergence time is incompatible with business requirements – or there is a low tolerance for random behavior in the initial stages of deployment – this flexibility in designing models and decisions is crucial.

In a future post, we will discuss another approach to amalgamate models, taking us to a whole new level of predictive modeling and analytical insights; combination models predicting likelihoods using multiple child models.

Friday Feb 17, 2012

Facet Based Predictions

The analytical models method detailed in a previous post are not only extremely valuable for reporting, but can also be used to predict likelihoods for things other than regular choices. We can for instance generate predictions based on statistics for an attribute of a choice, rather than the choice itself. We use the term facet based prediction to describe this advanced form of generating predictions.

This novel approach to modeling can be applied to significantly improve predictive accuracy and model quality. It can also facilitate the rapid transfer of existing learnings to newly created choices based on their facet values. These capabilities can be of use to practically all implementations, but they are of utmost importance in cases where the number of choices is very high or individual choices have short shelf life. In these instances, there might simply not be enough time or data to be able to predict likelihoods for individual choices. We could predict likelihoods for certain facets of our choices; as long as their cardinality remains relatively low.

Consider the following example in which we recommend products based on the acceptance of other products in the same category. In our ILS, Oracle Real-Time Decisions will be used to recommend a single product based on a single performance goal: Likelihood.

Choice Groups Setup

Products that may be recommended are stored in a choice group Products (we will use static choices, but this approach could be implemented for dynamic choices also). Product choices have an attribute Category which will contain a category name. We will use a second and separate dynamic choice group Categories to record acceptance of the different product categories.

Choice Group Setup

Note that we never intend to return any choices from the Categories choice group to a client. It is configured using a dummy source and will not contain any actual choices. This group is only used within the ILS for predicting likelihoods. Statistics for this group may however be viewed in decision center reports.

Recording Events

Similar to the example for analytical models, we will record events against a dynamically generated choice representing a facet value rather than against the actual choice. In this example, both the actual choice and the event to record will be passed through a request represented as Strings.

// create a new choice to represent the category facet
CategoriesChoice c = new CategoriesChoice(Categories.getPrototype());
// set properties of the choice
c.setSDOId("Category" + "$" + Products.getChoice(request.getChoice()).getCategory());
// record event in model (catching an exception just in case)
try { c.recordEvent(request.getEvent()); } catch (Exception e) { logError(e); }

Model Setup

Our model setup is practically identical to before, but this time we'll enable "Use for prediction".

Predicting Likelihoods

A function PredictLikelihood will be used to predict likelihoods for our products. The function takes a Products choice and an Event (String) as parameters and returns a Double value representing the predicted likelihood.
// get instance of the model used for predicting Category Events
CategoryEvents m = CategoryEvents.getInstance();
// return the likelihood based on the generated SDOId and the "Accepted" event
return m.getChoiceEventLikelihood("Categories$"+product.getCategory(), event );

 

Choice Group Scores Setup

On the scores tab for the Products choice group we configure the Likelihood performance goal to be populated by the PredictLikelihood function using parameters this and "Accepted". The keyword this refers to the particular choice being scored and will ensure each choice is scored according to its category facet.

That is all that is required to score choices against a facet. We can now create decisions and advisors that use these predictions to recommend products based on their categories.

In this example, we have predicted likelihoods based on a single product facet. As a result, products in the same category will be scored the same. In practical implementations this will rarely be an issue, because there will presumably be multiple performance goals. Also, likelihoods may be mixed with product specific attributes like price or cost; resulting in score differentiation between products regardless of equality in likelihoods.

In a later post, we will discuss how we can expand on this to include multiple product facets in our likelihood prediction.

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Issues related to Oracle Real-Time Decisions (RTD). Entries include implementation tips, technology descriptions and items of general interest to the RTD community.

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