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.

Does Personalization Pave the Cow Paths?

Michael Hammer, the father of business reengineering, famously used the phrase “paving the cow paths” to describe the ritualizing of inefficient business practices. Now the pervasiveness of personalization of our systems is being accused of paving our cow paths by continuously reinforcing our narrow interests at the expense of exposing us to other points of view. This latest apocalyptic image being painted is one of a world where we are all increasingly locked into our parochial, polarized perspectives as the machine feeds us only what we want to hear. Big Brother turns out to be an algorithm.

I commented on this over at Greg Linden’s blog, but wanted to expand on those thoughts a bit here. Of course, I could first point out the irony that I only became aware of the Eli Pariser’s book about the perils of personalization, The Filter Bubble, through a personalized RSS feed, but I will move on to more substantive points—the main one being that it seems to me that a straw man, highly naïve personalization capability has been constructed to use as the primary foil of the criticism. Does such relatively crude personalization occur today and are some of the concerns, while overblown, valid? Yes. Are these relatively unsophisticated personalization functions likely to remain the state-of-the-art for long? Nope.

As I discuss in The Learning Layer, an advanced personalization capability includes the following features:

1. A user-controlled tuning function that enables a user to explicitly adjust the “narrowness” of inference of the user’s interests in generating recommendations
2. An “experimentation” capability within the recommendation algorithm to at least occasionally take the user outside her typical inferred areas of interest
3. A recommendation explanation function that provides the user with the rationale for the recommendation, including a level of confidence the system has in making the explanation, and an indication when a recommendation is provided that is intentionally outside of the normal areas of interest

And by the way, there are actually two reasons to deliver the occasional experimental recommendation: first, yes, to subtly encourage the user to broaden her horizons, but less obviously, to also enable the recommendation algorithm to gain more information than it would otherwise have, enabling it to develop both a broader and a finer-grained perspective of the user’s “interest space.” This allows for increasingly sophisticated, nuanced, and beneficially serendipitous recommendations. As The Learning Layer puts it:

. . . the wise system will also sometimes take the user a bit off of her well-worn paths. Think of it as the system running little experiments. Only by “taking a jump” with some of these types of experimental recommendations every now and then can the system fine-tune its understanding of the user and get a feel for potential changes in tastes and preferences. The objective of every interaction of the socially aware system is to find the right balance of providing valuable learning to the user in the present, while also interacting so as to learn more about the user in order to become even more useful in the future. It takes a deft touch.

A deft touch indeed, but also completely doable and an inevitable feature of future personalization algorithms.

I’ve got to admit, my first reaction when I see yet another in a long line of hand wringing stories of how the Internet is making us stupider, into gadgets, amplifying our prejudices, etc., is to be dismissive. After all, amidst the overwhelmingly obvious advantages we gain from advances in technology (a boring “dog bites man” story), the opportunity is to sell a “man bites dog” negative counter-view. These stories invariable have two common themes: a naïve extrapolation from the current state of the technology and an assumption people are at best just passive entities, and at worst complete fools. History has shown these to ultimately be bad assumptions, and hence, the resulting stories cannot be taken too seriously.

On the other hand, looking beyond the “Chicken Little” part of these stories, there are some nuggets of insight that those of us building adaptive technologies can learn from. And a lesson from this latest one is that the type of more advanced auto-learning and recommendation capabilities featured in The Learning Layer is an imperative in avoiding a bad case of paving-the-cow-paths syndrome.