Qwhisper Is Looking to Solve Social Search With a Dose of Uber-Geek
Sometimes a start-up’s product is pretty, sometimes it’s from famous founders and occasionally it’s dead simple.
Qwhisper is none of those things — in fact, it’s barely even a product at this point. But its team of founders are attacking a devilishly hard problem.
The company and Web app of the same name attempt to search and categorize social media updates with an accuracy that even the sector’s giants have been unable to deliver thus far.
“Search for social is really tough. When someone mentions Mars, you don’t know if they mean Mars the planet, the god, Bruno Mars, the rover, or the candy bar,” said Qwhisper co-founder Eldar Sadikov. “With Web pages, there are all kinds of context clues to help you figure things out, like links and other data. Social content is just so much shorter — you have to be very sophisticated to [make sense of it].”
What that means for us avid Twitterers is that, as of now, searching for a category of tweets is not a useful endeavor — and forget about searching for tweets about a simple but amorphous topic such as “popular music.”
But Sadikov’s Qwhisper, which is in private beta, makes use of some new search algorithms to reorganize a user’s social streams.
Its founders claim the search and sort technology of Qwhisper can reliably deliver tweets to the user based on a topic, category and search term.
So, how does Qwhisper do it?
Sadikov made an attempt at outlining just how complex it is for a computer to make sense of a stream of single tweets:
“You need much more sophisticated natural language processing technology [for social] than what is needed for Web pages. [The system must] understand words like “lol,” “cuz,” “gonna,” “gotta” — because there is so much colloquial language in social content, compared to Web sites.”
Only after dealing with those problems, which are in themselves complex enough for several research papers, can Qwhisper layer in the really complex processing to answer such contextual questions as: What does this person do normally? And, what does that person normally talk about?
But every start-up with a search component boasts custom algorithms, so why should users be confident that Qwhisper’s are superior?
Qwhisper is touting the company’s intellectual pedigree.
Sadikov and some of the other co-founders left their PhD programs at Stanford’s InfoLab to start Qwhisper — the same InfoLab where Larry Page and Sergey Brin developed some of the early parts of Google.
Sadikov also spent time at Google, where he worked on building an algorithm for organizing small sets of words together in contextually relevant groups.
Not too long after, he gathered a group together to launch Qwhisper using some of the same concepts.
If Qwhisper or the engine that powers it proves successful, the consequences could be far reaching.
Delivering tweets and other social content in contextual channels could mean a whole new class of applications — and advertising — all built around social content.
But complex graph-modeling and multivariate algorithms aside, the litmus test for Qwhisper will be simple user interaction.
“Ultimately, if I post something like ‘saw inception last weekend – amazing,’ the system needs to recognize what that is about … even though it says nothing about movies or genre,” said Sadikov.
I caught him and one of his co-founders, Montse Medina, at the recent Stanford StartX incubator demo day to talk more about Qwhisper: