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.
Yes, 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.
(c) 2012, GigaOM.com.