SAGA – Offers a mobile companion app that records your activities in order to give you personalized feedback about possible future activities. So SAGA is a personal recommendation agent, which helps you to organize your day and gives suggestions for restaurants at lunchtime or for great places you could visit. SAGA is continously learning your habbits in order to improve the quality of recommendations. It also records and measures the actual activity and the actual context, such as the weather or who is nearby.
Glancee, which was recently acquired by Facebook in May 2012, is another good example for a recommendation engine that uses location information in combination with the network community Facebook. The goal of Glancee is to recommend new people which are sharing the same location, who you didn’t knew before.
Uncovet.com, a design oriented eCommerce platform is launching to the public in the next days, including a new recommendation engine for analysing your type of style in order to build a ‘style graph’ for you. The style graph should enable very specific design-centric offers for their customers. Uncovet will use social shares and purchase history for building your own, very specific style graph. This solution is called spontaneous recommendation engine, such like Fab and Ofakind.
For me recommendation engines are the actual hype topic at the moment. Platforms like Spotify or LastFM recommend the user the next music track to stream, Amazon and ebay are recommending products and others are recommending new friends or people to meet.
The company considers itself a spontaneous recommendation engine like Fab and Ofakind, except focusing on personalization, current trends and seasons vs. inventory it needs to unload. Like Fab, users get rewards and discounts for signing up their friends and followers. → Read More