artificial intelligence, Semantics

What Netflix Could Do with Its Recommendation Engine to Excite Me as a Customer

You Might Like House of Cards, But I Couldn’t Possibly Comment

My colleague Peter Sweeney, the founder of Primal, and I were talking recently about Netflix using AI, specifically deep learning algorithms, as part of efforts to further improve its recommendation engine. I’ll admit, instead of being excited at the prospect of more insights being gleaned from my viewing history, my first reaction was concern about yet-another bubble along the lines of, or even worse than, the infamous search engine filter bubble, only this time for recommendation engines.

True, we learned earlier this winter that Netflix has re-engineered Hollywood. Netflix has a very rich and extensive categorization scheme — the product of analyzing movies and TV shows from an amazing number of angles, and presumably also following the trails of relationships that customers perceive among the films and TV shows that they view. I think Netflix provides good recommendations, certainly better than they did a few years ago. But frankly, the recommendations still hit dead ends quite often and its easy to get stuck in the rut of more of the same old thing. So my fear upon reading about what Netflix is doing was basically this: deeper mining equals even deeper rut — even more of the same old thing. And that could easily be the case. Just going deeper into analyses of the content itself, as well as my past preferences for it, might well add more categories to their classification scheme, but it doesn’t endear me more to Netflix if I still just get recommendations based largely on my past preferences, only now using more specialized categories. I’m still stuck in a rut.

What I want to experience is more of what I like to call ‘designed serendipity’. If Netflix or Amazon or one of their peers are truly uncovering deeper and more nuanced patterns, particularly within the content itself, but also about my viewing preferences, then they could use that new data to make the recommendation experience more interesting and more compelling for me — giving me something I could actually get excited about. How could they do that? They could start by proposing content from adjacent categories based on walking their classification scheme. Because there would presumably be more, finer-grained categories, exploring some of the neighboring ones could add some fun while still keeping the risk low of jarring me with an off-the-mark recommendation. They could also take those lower-level elements and apply them in somewhat different contexts, preserving the elements that I like, but also mixing in some new twists. They could even try combinations of the lower-level elements, as they’ve done fairly successfully already at higher levels of their classification scheme.

Let me use some examples to illustrate the sorts of things I’d like to see. The West Wing and House of Cards are both political dramas. But at a deeper level, The West Wing is much more about the camaraderie of the White House staff as a team, with politics and political intrigue as more of a plot device for the personal interaction. House of Cards on the other hand is more of a psychological thriller set in a political context. The political maneuvering and bold back-stabbing are core to the show — and for me at least, that’s what makes it fun to watch. Those are fairly subtle, but significant differences that if deep learning can expose, would establish its customer value. Put another way, just because I like House of Cards (the Netflix version, but even more so the original BBC version), does not mean The West Wing would be a good recommendation for me, since such a recommendation is based on more superficial similarities between the shows. I’m a friendly, collaborative, team-oriented person in my real-life, so I’d rather see ego-maniacal scheming and back-stabbing as part of my diversionary viewing!

If Netflix could ‘context lift’ those elements of House of Cards that I do like and then reapply them in different contexts, that would excite me. For example, because I like House of Cards, I might like The Tudors better than The West Wing, even though The Tudors is a historical drama. The Tudors has more of that scheming and back-stabbing (or head chopping!) that I like. While it’s a political drama of sorts, it isn’t in the same sense as The West Wing and House of Cards, so it might not come up as an obvious recommendation. To make that recommendation is more deep and subtle. I also happen to like The Americans, a drama that is political only in an espionage context, but again also a thriller with lots of unexpected twists and turns. And I hate to admit it, but I also like Revenge. Revenge has almost nothing to do with politics, but shares the dark scheming and plotting of House of Cards. Would Netflix be able to recommend either of them to me based on House of Cards? If they get to that level, they’d have my customer loyalty.

At that point, the only thing still missing for me would be adding even more pleasant surprises — turning down the ‘designed’ aspect and turning up the ‘serendipity’. What if I want something that’s conceptually related to a past viewing interest, but still quite different? If I watched Planet of the Apes (particularly the original), wouldn’t Jane Goodall‘s documentary for Animal Planet, Almost Human, be an interesting recommendation? It would for me! Or what if I want to broaden my horizons and try something completely different than what I’ve been watching? Can Netflix put my past preferences in a blender and recommend something really novel and out-of-the-ordinary? Or alternatively can I just throw at Netflix some topics that I’ve been thinking about or have a point-in-time interest in and get a recommendation made-to-order at that particular moment, based on what I just provided, or perhaps subtly influenced by my viewing history? Do those things, Netflix, and I might become a loyal customer for life!

Tony

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