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Learning from Netflix

Netflix recently published a blog that lays out some of their experiences with their recommender system.  The blog is notable in that Netflix was one of the pioneers of e-commerce recommendation engines, has one of the most famous recommendation engines, and packs a lot of details and good insights into the blog.

Here are a few takeaways:

75% of what people watch via Netflix is due to recommendations.  And given how impressively recommendations drive sales for businesses such as Amazon, it is not surprising that sophisticated recommender systems are becoming the norm in e-commerce.

“Everything is a Recommendation.”  Netflix uses this phrase to underscore the point that most of its interface now personalizes to the user. This approach is an inevitable direction for user interfaces most generally since it is clearly technically feasible and delivers business results.

Optimize for accuracy and serendipity.  People are complex and have diverse tastes, moods, etc.  A good recommender will try to help recommendation recipients keep from just re-paving their own cow paths.

Diversity of behavior types trump incremental algorithm advances.  Given already advanced algorithms, recommender improvement comes grudgingly if based on limited behavioral types (e.g., just course-grained ratings). Deriving inferences from a greater diversity of behaviors, as well as contextual cues, delivers greater advantages.

Explanations of recommendations are critically important.  Recommendations that are accompanied by explanations as to why the recommendation was delivered to the recipient are perceived to have greater levels of authority and credibility, and promote trust.

These are important takeaways for e-commerce, but they are just as applicable to enterprise adaptive discovery systems. In fact, as I discuss in The Learning Layer, because there are often more behavioral types, as well as topical structures, with which to work in the enterprise environment, adaptive systems in the enterprise have some inherent advantages versus those in purely consumer-facing environments.

I am often asked by executives about the value proposition of adaptive discovery or learning layer systems in the enterprise. A moment’s reflection on the ability to deliver the right knowledge and expertise to the right person at the right time generally suffices. But from a macro-economic standpoint, simply observing how recommendation systems have transformed e-commerce also should make the point.  On-line businesses without world-class personalized discovery capabilities simply cannot hope to compete going forward. The same lesson surely applies to systems within the enterprise.

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