Restaurant recommendations service Ness has been steadily improving upon on its technology which helps you find the best place to eat based on your unique likes and dislikes, a place’s popularity, time of day and other factors. Earlier this year, it rolled out an update which allowed for instant recommendations, and today it’s launching recommendations for groups – that is, it’s able to combine several people’s personalized recommendations to come up with a list of places that everyone can agree upon.
In addition, Ness is no longer just an iOS app, as Ness is now available on the web and mobile web, the latter which allows the service to work on Android for the first time.
The Ness website is actually available at likeness.com, as the Ness.com domain is already taken. According to co-founder and CEO Corey Reese, the web versions (desktop and mobile) have been slowly rolling out since this summer, but they had only reached feature parity with the iOS app over the last couple of weeks.
On the Ness website, users can sign up with their email or Facebook account, or just browse through the available options to see what’s nearby. Each restaurant’s profile page offers the usual info, like phone, address, hours, map, price point ($$), and ratings – in this case, via Foursquare. But pages go a step further, too, by suggesting similar options to the venue you’re viewing, as well as letting you know what the restaurant is best known for (e.g., “ladies night,” “great shrimp,” etc.) based on comments found on social media, like Instagram.
Also on both the web and mobile, the list-making feature has been improved so users can see their saved places on a map – handy for planning your itinerary while on a trip, or just to get a better sense of where places are, geographically.
Meanwhile, with the focus on group recommendations, which is new today, Ness is positioning itself as more of planning tool for scheduled outings, rather than an app you’d grab at the last minute while looking for inspiration, or to find something you might like when in unfamiliar territory.
To use this option, you can tap a plus sign to add friends you follow on Ness to group, up to nine people. Reese says that for now, everyone in the group has to already be a Ness user, but soon it will work with any friend on Facebook or Foursquare too, by puling in their check-ins.
The group feature doesn’t just let you see which places users have explicitly favorited, but it also shows in more detail why they like something – for example, it’s on a list of places they want to try, or it will even how much they like a restaurant as a percentage, based on Ness’s understanding of their interests and activities. Group members can then vote on their favorites to help come to a consensus.
“[Group recommendations are] something we’ve wanted to do for a long time – since we first built the product,” says Reese. “But it’s only now that we have enough data where we feel comfortable that the quality of the recommendations for multiple individuals would really be good.” The company is not disclosing its user numbers or actives, but says it has over 6.5 million user ratings across its service to date.
The company touted this technology as the first recommendation engine built for groups, but I seem to recall Clever Sense’s Alfred app doing this back in fall 2011. (Clever Sense was later acquired by Google, and folded into Google Places. The team has since moved to Google Now, we hear.) Also like Clever Sense, Ness, has had similar acquisition offers. But the company also has $20 million in outside funding, and can continue for years.
The company has yet to focus on revenue, though Reese could eventually foresee Ness using a sponsored search model at some point, as well an expansion in affiliate revenue, as with the OpenTable bookings the app allows for today. But to get there, Ness has to first carve out room for itself on ever-crowded smartphone homescreens where general purpose apps like Yelp, Google Maps, and even Foursquare still dominate users’ time and attention.
Reese believes Ness will succeed by doing one thing and doing it well. “We’re making the best restaurant recommendations possible for every situation that you’re in, and taking into account all the variables when you’re making that decision,” he says.