Summary:  In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance. 

In our first article, “Understanding…

Summary:  There are many sources of packaged recommenders including the more comprehensive Digital Personalization platforms.  It’s also possible to code your own.  Here are a few things to consider.

In our first article, “…

Summary:  There are five basic styles of recommenders differentiated mostly by their core algorithms.  You need to understand what’s going on inside the box in order to know if you’re truly optimizing this critical tool.

In our first article, “…

Summary:  In this multi-part series we walk through the full landscape of Recommenders.  In this article we cover business considerations as well as issues for Recommenders as a group.  In the next articles we’ll discuss the details of the five major types of recommenders, improving their performance, and finally the coming impact of deep learning on Recommenders.

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Summary:  As deep learning expands those capabilities are finding their way into the not-for-profit community in the service of conserving the earth’s wildlife and forests.

The for-profit world may be driving AI but it’s a solution to many problems in the not-for-profit world as well.  We were particularly impressed by the use of…