The company's proprietary recommendation algorithm suggests content to a user based on his/her online viewing habits, in a similar way to competitor Outbrain.
What's interesting is that for now Buffer has opted not to use any algorithms to suggest content.
The feed itself is going to be filled with significantly more algorithms to suggest content to you — both organic and sponsored, "native" content — a new ad unit that LinkedIn is hoping to expand.
On one side, the startup has developed algorithms to suggest people and blogs to users based on a ton of metrics, and on the other side there will be some sort of editorial spotlight with Tumblr staffers suggesting content that "really stands out", helped by community activity.
Right from building algorithms to surface the right suggested content for users, to providing the best related recommendations and analyzing internet connection speeds.
Whether the issue is big data and profiling, or search results and suggested content, it is the algorithm that gets the blame.
Do you want to suggest content?
At Netflix, algorithms suggest the genres of movies to commission.
Taking your browsing history, and then pushing it through its algorithms, Lumi suggests other content and other sites that will be relevant to you, presented in a series of Pinterest/Fancy-style, image-led boxes that populate infinitely down the screen.
It also offers offline reading, search and discovery tools for finding newspapers, catalogs and magazines, and an improved personalization algorithm that suggests new content based on your previous reading.
The developed algorithm suggests new tags that can be associated to video content, based on the visual similarity of frames extracted from social websites like YouTube and Flickr.
