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Summary:

A newly-launched startup called Foundd is taking the algorithmic approach, while its Berlin neighbour Tweek.tv is going for the social angle. But why has no winner emerged in this space already?

Tweek.tv user

Do you need someone to tell you what films to watch? Is it hard to discover content? There’s a whole sector of services evolving to target those who reply ‘yes’ to such questions. And when I say evolving, I mean trying, and then — at least so far — mostly failing.

Tweek.tv userYes, we have Flixster and the like, but there we’re talking dense databases rather than properly personalized recommendations. And the fact that there’s no winner yet in this space – apart from intra-platform discovery engines such as Netflix’s – could mean one of two things: either that not many people actually want such a service, or that nobody’s got it right yet.

At least two Berlin startups hope the latter is true, but the approach each one is taking is very different. In the social-driven discovery corner we have Tweek.tv and then, as of yesterday, in the algorithm corner we have Foundd. Which idea works better?

“We use an algorithmic approach to calculate personal recommendations for each individual,” Foundd’s Lasse Clausen told me. “While there’s definitely the element that you want to see what your friends have seen, they’re generally not as good as algorithmic ones because if you test it, there’s a surprisingly small number of friends that really share your taste.”

Boom! Over to you, Tweek.tv:

“We believe there is nothing stronger than a recommendation from a person you trust and whose taste you know when it comes to movie recommendations,” co-founder Marcel Duee said. He noted that, although Reed Hastings owns the best algorithm in the world after the Netflix competition, when Facebook published the package of Social, Open and Interest Graph, Hastings said it trumped the algorithm. “We also we see that click-through rates for social recommendations are significantly higher than for pure algorithm recommendations,” he added.

Pow! There’s a valid point there — and Netflix has more than 900 engineers working on its recommendation algorithms. But then again, Foundd is cross-platform (it searches across both Netflix and iTunes). Back to Foundd’s Clausen:

“Netflix put a lot of effort into predicting how much I’ll like every one of their movies. But I don’t really care whether I’ll dislike a movie with 2.3 or with 2.35 stars, it doesn’t help me find a good movie. We focus on giving more accurate predictions over a smaller number of movies and those that you’ll really like. Also, Netflix doesn’t give recommendations for movies that several people will enjoy together.”

Clausen also pointed out that Foundd’s longer-term vision includes being a recommendation engine for a variety of content, including TV shows (like Tweek.tv) but also apps, games and books. “So you even if you only rated movies, you’ll be able to get recommendations for books or games on your iPad for example,” he explained.

So to sum up, one side thinks friends are the most useful arbiters of taste, while the other prefers to put its faith in the user’s own demonstrated taste (via a lengthy series of film ratings at sign-up). I can see advantages and problems with both approaches.

The social recommendation engine has the advantage of ease: simply plugging your friends into it tells the service what it needs to know, without the need for a questionnaire. But on the other hand, you may like your friends much more for who they are than for their tastes.

The algorithm approach is much more personal, which is a strong advantage, but it also requires a lot more work on the user’s part. Also, it’s really hard to take on algorithm beasts such as Netflix.

Still, this all brings us back to the issue of the market. I can’t help but feel skeptical about it: the idea has been around for a few years now, so shouldn’t at least one cross-platform recommendation engine have gotten big by now?

Could it be the case, especially with paid content, that people invest both their time and money in one or two platforms and are comfortable enough within those confines to not require third-party discovery?

If you ask me — and the corpses of services such as Sortflix and MyZeus — the jury’s still out on this space.

  1. Lasse Clausen Wednesday, July 11, 2012

    Good in depth analysis of the space.
    I’d like to add that Netflix doesn’t own the best recommendation engine in the world because they never implemented it. http://www.techdirt.com/blog/innovation/articles/20120409/03412518422/why-netflix-never-implemented-algorithm-that-won-netflix-1-million-challenge.shtml

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  2. People don’t want to go out of their way to be suggested something – they want it as part of an experience. Netflix is a great service experience: you go there to watch films, not to be suggested films – there’s a higher need.

    Foundd et al are going to find it hard to survive by building “discovery” services without going the whole hog and building the service too.

    And companies wanting to implement personalised recommendations don’t have to hire 900+ engineers like Netflix do (or, as @xamat says, don’t). Software as a service companies like PredictiveIntent have APIs that anyone can connect to, configure how the recommendations are decided, and request sugestions back to the individual user.

    So, to sum up – discovery and recommendations are part of an overriding experience and won’t survive as disconnected services.

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    1. Lasse Clausen Wednesday, July 11, 2012

      I agree that easy trumps everything in entertainment. And in the former, channel oriented environment people first went to the source and had to take the best that was available, getting something better involved too much effort.
      Now we’re in the middle of a shift to an object oriented environment and the rise of long tail. Distribution channels (what you call “the experience”) have become commodities and the object is what matters most.

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      1. I disagree. Experiences aren’t commodities, they’re the most important part of modern b2c business and co’s that think otherwise are pretty much destined to fail into obscurity.

        The only way these co’s an live is if they are fixing a need. E.g. Chomp, bought by Apple, fixed the need for discovering apps. Yelp helps me decide where to go out for dinner. Zite finds me stuff to read. But like I said, there’s an overarching experience that fulfils a need over just recommendations..

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  3. Daniel Bourdeau Wednesday, July 11, 2012

    I would love to see a cross-platform recommendation system that works well. I believe there is plenty of insight to be gained from the types of books, magazines, video games, music, and movies I consume.

    I would really like to see a mixed platform that would let me pick/rate some of my favorite books, movies, tv shows, etc. and then select a list of my friends who’s taste I trust. I can honestly say I don’t want my entire Facebook friend’s list or Twitter feed analyzed to get recommendations. As the author accurately stated above, there are a lot of people who I like to hang out with, but wouldn’t take a recommendation from.

    Anyway, thanks for the post. Very interesting!

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  4. Seems like all the recommendation engines are trying to solve a problem that doesn’t exist. With over 5 hours of TV viewing each day, the average person has no problem finding something to watch. And maybe TV is like chocolate. If you find a good show you only need to watch one, if you watch a bad show maybe you need to watch another one to be fully satisfied. So a good recommendation engine might result in less viewing.

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    1. Lasse Clausen Wednesday, July 11, 2012

      For people who are used to linear TV I’d agree that there’s not much need for recommendations. But a large number of American college graduates in recent years don’t get cable subscriptions anymore, only an internet connection that they source their entertainment through. In that case you really need a mechanism to filter out the noise while maintaining the easy, lean back experience that we want for entertainment.

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  5. The article states what we know for a long time. Every reco approach has it pros and cons. Relying on a single method, be it collaborative filtering, social recommendations, semantic or anything else is bound to create frustration. Why? Since any method has its own lock-in mechanism. For example Youtube related videos will initially please you as it bring more of what you liked but at some point this inward spiral of more of the same becomes displeasing. This has been shown in research by Dr G. Oestricher and S. Riechmann from TAU university who demonstrated that adding user-links (social) to content links (related content) yields in overall satisfaction increase of more than 30% (see research – http://goo.gl/0wQhu)

    In short, engine diversification (or as called by VO: Engine blend) promise to improve user satisfaction and overall usage conversion substantially

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