MG Siegler at TechCrunch to write for the 2009 year. It covers web, mobile, social, big companies, small companies, almost all. And Apple. A lot. Prior to TechCrunch it covers different technologies beats for VentureBeat. Originally from Ohio, mg attended the University of Michigan. He previously lived in Los Angeles, where he worked in Hollywood and in San Diego where ... ? Read More
Last night I went out for lunch. But I didn't get where I was going, Ness did. Location was good. Only one for Ness.
We previously covered Ness calculations in July, when talking about technologies that will power their possible applications (as well as their funding). Today, the first such app has gone live in the App Store, called simply, Ness. This is a personal search engine, meals in restaurants. And that's fine.
While most of the food and restaurant recommendation apps focus on ratings from the general population, Ness adapted around social, as well as your own taste. Others have tried this before as well, but Ness seemed to have nailed it thanks to a ton of data they've been pulling up the app even started.
They can do this because they hook into Facebook, Foursquare and Twitter (as well as several other smaller sources) find signals some of the restaurants. People tweeting about liking them? Users check out there much? These sorts of things. This helps determine which restaurants Ness must show you within an application.
When you first load up the app, you are asked to assess ten places you have visited. This helps calibrate points "Likeness" of the key recommendations. Those scores likeness appear as probability percentage that you like the restaurant shows. This, together with the proximity and social data to determine what restaurants are provided to you.
But the application from a search engine in the more traditional sense. You can easily search for different types of food, whether General or specific. And you can choose to search in other cities. Or you can change the search options to limit your search by price and weed out the more circuits, for example.
When you find your favorite place you can go deeper to get hours, phone numbers, website, etc. you can also save space to return later. And you can share restaurants with friends with the click of a button.
They key to app, though, is the ease of use and data accuracy. I constantly find similarity ratings were roughly correct what my actual rating be for restaurants, I was in and the system will only get better, how do you appreciate more space.
The appearance of the application is also beautiful. I said that we can thank the initial team member iPhone Apple previously and now with Ness, to do so.
Remember that restaurant recommendations just step one of Ness hopes to offer. Next steps will indicate similarity engine to shops, music, nightlife and entertainment. All that can be done in one killer, subjective mobile search system.
You can find the Ness in the App Store here. Unfortunately, it is only for United States at the present time.
Ness mission – to make calculations of personal search. Combining an understanding of human nature on their experience in search, recommendations, and social networks, the company can deliver the experience ...
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