Video search provider MeFeedia has introduced a new iPad app that seeks to make it easier to find and share interesting videos online. Unlike other apps, MeFeedia hopes to surface popular and interesting videos based on user ratings and interests without hooking into social networks or graphs.
The app seeks to introduce viewers to new videos, which they can choose to watch, bookmark to watch later, “Like” or “Dislike.” Users can also share videos with friends and followers on Facebook or Twitter. Navigation is super-simple, as all one has to do to choose videos is swipe through them on a preview pane in the bottom third portion of the app.
Users can also specify which categories of videos will be shown to them through the settings tab, allowing them to choose between categories like news, comedy, tech, business and of course the most popular clips. And they can check out their own viewing stats in the stats tab. (I, for instance, have watched a total of 14 videos within the app, spending 33 minutes and 43 seconds doing so.)
The goal, according to MeFeedia CEO Frank Sinton, the app is meant to easily introduce users to snackable content — which is one reason that there’s no real search functionality. Instead, MeFeedia’s app encourage users not to find long-form content they know they want to watch, but to discover new short videos they might not have known about in the spare moments of the day.
Of course, there are plenty of apps out there for checking out new videos — but most of them are centered on surfacing videos that your friends have liked or shared from social networks. MeFeedia hopes to introduce viewers to new videos not by connecting them with things others have shared, but by learning what they like and trying to guess which videos they might enjoy. As a result, most videos first displayed are in the “popular” category and recommendations improve as you tell it what you like (or don’t like).
This is the first iteration of the app, so it’s a little rough around the edges. But MeFeedia hopes to update with more personalization in the future (as well as less purple). And, most importantly, it hopes to use the implicit viewing data that comes from the app — as well as the explicit likes and dislikes — to make more intelligent recommendations as it learns how to identify user affinities and viewing patterns.