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The Recommendation Revolution, Continued . . .

That “hidden in plain sight” IT revolution of ubiquitous, adaptive recommendations continues to accelerate. Jive Software, a leader in enterprise social software, has just acquired Proximal Labs to bolster their machine learning, recommendation engine, and personalization capabilities. Given that Jive is reportedly preparing for an IPO, this move is a major endorsement of the key theme of The Learning Layer–that adaptive recommendations will be a standard part of enterprise computing. It also is another strong signal that, as I recently wrote, we are going to rapidly witness a standard, adaptive IT architecture emerge in which a layer of automated learning overlays and integrates the social layer, as well as the underlying process, content, and application layers. And I would be remiss if I didn’t mention our own initiative for bringing the learning layer to Microsoft SharePoint and other enterprise collaborative environments, Synxi, which includes a suite of auto-learning functions such as knowledge and expertise discovery, interest graph visualizations, and recommendation explanations.

Another recent and important signal of the recommendation revolution is Google’s recently announced “+1” function, an analog to Facebook’s “like” button. Google will use this tagging/rating function in generating responses to search requests. Google was the pioneer in using behavioral information to improve search results, and this is just the latest, and most dramatic, step in increasingly relying on behavioral cues and signals to deliver personalized search results that most hit the mark.

That is why, as I discussed in the book, responses to search requests should really be considered just a certain kind of adaptive recommendation, one in which more intentionality of the recommendation recipient can be inferred by virtue of the search term or phrase, but that is otherwise processed and delivered like any other type of recommendation. And it is why search processing and recommendation engines will inevitably generalize and converge.

Stay tuned . . .

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