The startup instruction booklet, powered by my life (and other things I think about).
Saturday, March 28, 2009
LikeMe and Hunch
I guess it's the week of the personalized search engine. Which bodes well for what we do. It's always good to have competition in the same space, especially as you raise money. If one company grabs some cash from a VC, others VC's tend to want in on a competing technology. They don't want to get left in the cold if something hits. It also adds credibility and publicity to our technology.
We initially called ourselves a personalized search engine - but felt that moniker didn't really capture what we were doing. Personalization is certainly core to what we do, but we consider ourselves more of a discovery tool, something that helps build trust and relationships to other things on the internet. "Things" are relative - it could be other people, objects, services... or really anything that you can define to have a personality.
LikeMe is a "personalized recommendation engine" that "makes it fast, easy and fun for you to discover new places to eat, drink, dance, sleep, shop, ride, relax, primp and explore. It’s a great tool for the city you live in, and an even better tool for when you travel. It’s like having a great network of friendly recommenders all across the country." They "do" what we aim to "do", but we like to think our tool is much more powerful and targeted. While they match you to other people, I'm not really sure how their matching algorithm really works? So it left me with lots of questions - rather than answers.
As for Hunch:
"Hunch is a decision-making site that gets smarter the more it's used.
After asking you 10 questions or fewer, Hunch will propose a concrete and customized result for hundreds of decisions of every kind: What kind of car should I buy? Should I switch to a Mac? Should I dump my boyfriend? Where should I go on vacation? Should I get a tattoo?
Hunch uses machine learning to get smarter in two ways:
* User contributions train Hunch to be smarter overall. Contributions can take many forms, from correcting a fact that Hunch got wrong, to suggesting new decision topics to feature, follow-up questions to ask or decision results to propose.
* The more Hunch learns about each individual user's personality and preferences, the better Hunch can customize decision results for that user. It's like a friend getting to know someone's taste and preferences over time, so they can provide sound and trusted advice."
I'm excited about this space...things are heating up!
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