How Netflix should improve recommendations

What a Netflix companion app would look like

Thomas Tennyson
May 22, 2018 · 6 min read

We’ve been critiquing (and generally complaining) about recommendation engines on The Fourth Wall for a while now. It’s about time to put my neck on the line and offer some ideas on how to make them better.

Some of the problems I’ll address were outlined in a previous article “Are recommendation engines getting it wrong?”. If you haven’t already, I encourage you to give it a read, it’ll add more context to the points below.

To be clear. I’m taking aim at Netflix because they seem to be the leader in recommendations at the moment. The same points can apply to any OTT video platform.

Let’s dance…

#1. Making profiles more personal

Your Netflix profile is corrupt. I don’t mean it’s a dirty politician taking money in fat brown envelopes. I mean the data contained in the recommendation profile it has built for you, is likely somehow wrong.

This can happen in many ways. But it boils down to this… What we watch changes depending on who we’re with. Signing into your account on a TV means Netflix will never be able to record that context to your profile.

Achieving a higher level of personalisation means a user’s profile must be immune from all outside influences.

What if we remove profiles from the TV altogether? The best place for them may be in our pockets. Imagine a companion mobile application that knows what you’re watching, receives only your feedback and is with you wherever you go. For fun, lets brand it “Netflix Recommends”.

Walk into a room with Netflix on the TV and it automatically connects. Better still, when more than one user connects to the same TV. Netflix can analyse each profile and find the shared preferences between them. Recommending the perfect content for that audience.

Each audience member can react separately to the content they’re watching. Meaning they can start building their own, hyper personalised profile.

The app also works as a controller. No more searching for the TV remote down the back of the couch.

“Netflix Recommends” knows what you like, and who’s watching with you.

#2. Thumbs Up, Thumbs Down

Never recommend me content based on something I haven’t specifically liked. There’s a hundred reasons why watching something doesn’t mean you liked it. Instead, steps should be taken to encourage users to add context to their opinion.

What does reacting to content mean? Giving a generic thumbs up? What’s the baseline for this expression? If you gave your absolute favourite movie a thumbs up, how do you express that you generally liked something, but didn’t love it.

This makes the ‘why’, seriously important. When you dislike a song on Spotify’s ‘Discover Weekly’ playlist, they ask you to clarify if it’s the song or the band that you didn’t like.

It’s possible to like an artist, and dislike one of their songs. Well done Spotify.

If we apply this logic to Netflix, it admittedly creates more complicated interactions. But getting to the root of why a user liked a series, is far more beneficial than 100 generic thumbs up.

A star rating system provides way more context, and gathering additional info allows us to understand the ‘why’.

Now we have a solid data set to start building our recommendations on.

#3. Transparency

How can we gain a user’s trust if we keep them in the dark? Designing transparency into a recommendation interface can be advantageous in a few key ways.

Feeling in Control

Netflix has revealed how they recommend content to users based on what “taste community” they’re part of. These are groups of users that tend to watch the same type of content. But Netflix doesn’t tell us what community we belong to, let alone allow us to change it.

I say draw back the curtain a little, let us see what’s going on behind the scenes. Allowing users to confirm or refute any conclusions that are being drawn is the level of control we should aspire to.

Review and edit any choice you’ve made.

More room for error

Recommendations will never be 100% “right”. But what if a user understands why something was recommended? Surely they’ll be more likely to view a discrepancy as an outlier. Otherwise, in the user’s mind it’s something that’s inherently wrong with the system.

To Netflix’s credit, they already do this to some of their content…

Adding transparency via the “Because you watched Step Brothers” label.

What might taking this a step further look like? Now that we have more context from user reactions, we can be more pointed. “Because you love Spoof Comedy Movies”. “Because you liked Will Ferrell in Step Brothers”.

Can you think of any more? Let us know in the comments.

Appreciation

If Netflix recommended me a series or movie that I then became a huge fan of — let me know! I’m human, I don’t remember why I started watching something, but I’d certainly value Netflix more if it did.

Between each episode is a good place to remind the user if they started watching this as a recommendation.

Validating the recommendations (even if they were bad ones) tells the user we’re learning and adjusting to their tastes. Part of the interface’s job is to build trust between the user and the system.

Time to recap

If I had some sort of leverage over Netflix, this is the list of demands I‘d send to whoever is in charge of the recommendations department…

#1. Zero ambiguity, profiles should be an accurate representation of our tastes.

#2. Let us give contextual feedback, like and dislike just doesn’t cut it.

#3. More transparency—we want to be able to trust the system.

#4. Bring back Marco Polo

That’s not so bad, right? I’m a reasonable guy. Can you think of something else that could be added to the list? Disagree with something here? Let us know in the comments below.

We’d sure love to hear what someone at Netflix Technology Blog or Netflix Design Team thinks of the above. :)

Here are a few of our team’s recent articles related to this topic. Follow us to keep in touch!

Thanks for reading! Please let us know your thoughts in the comments below. Your claps are always appreciated.

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