Friends recommend products and services to friends all the time. Now, a new mobile-device app is trying to capture that social-recommendation energy, and is trying to get noticed above the pack of recommendation tools.
The name of the app, and the company that is releasing it, is Wikets. The company said the goal is to put "your friends' best product and place recs at your fingertips," rewarding users for the best recommendations. Those recommendations can be diverse, such as first-time parents recommending to other new parents products for babies, restaurants that can accommodate children, or a great pediatrician.
The recommendations can be sorted by proximity, or they can be viewed in a stream, a la Facebook. The most popular recommendations are shown, and users can comment on them or save to a Wishlist. Products from online stores can be linked, such as from iTunes, eBay, Amazon, or places on Yelp or Foursquare can be suggested.
When a product or service is purchased following a recommendation, the recommender acquires reward points from online retailers, such as iTunes and Amazon. The purchases are made outside the Wikets app, but the purchase is tracked by Wikets, and the company gets affiliate fees for purchases. Points are also offered for making a recommendation.
Wikets, which has received venture capital from Battery Ventures and Andreessen Horowitz, was started by veterans of BladeLogic, a provider of data center and cloud automation software.
While there are a variety of review and recommendation sites, and check-in apps, Wikets is attempting to merge rewards with personal networks to replicate the kind of word-of-mouth -- and resulting satisfaction when you make a good recommendation -- that naturally takes place.
But Wikets is not alone in this quest. For instance, another start-up called Ness, short for "likeness," launched a mobile app in August that makes recommendations of restaurants based on friends with similar likes.
Users rate restaurants in a neighborhood, and they can search for restaurants nearby or by a particular cuisine, such as Chinese. A Likeness Score appears next to each restaurant, a rating from 1 percent to 100 percent, which is created from the user's stated preferences, the preferences of friends and the popularity of the restaurant. The listings also reference reviews from a user's Facebook friends or Foursquare check-ins.
Unlike, say, Yelp, Ness is not dependent on your friends writing reviews of the restaurant you're investigating. The company, which utilizes social-graph data mining and natural-language processing, is looking to expand to cover other products and services, such as concerts or retail stores.
Another new mobile app, Oink, has users review products inside establishments, such as a specific dish in a restaurant. So, instead of having to search for Thai restaurants to infer the best pad thai, one searches directly for the nearest, best example of that dish.