Watch this really interesting presentation by Coen Stevens, who explains how to build a recommendation engine for Wakoopa. Coen, who achieved his master degree at the TU Delft on Knowledge Based Systems and propedueses for Psychology and Philosophy at Leiden University became the Lead Recommendations Engineer at Wakoopa. He held this talk at recked.org.
Wakoopa is working on methods to understand what people do in their digital lives. In a privacy conscious way, their technology tracks what websites customers visit, what ads they see, or what apps they use. Users of Wakoopa are able to analyze that collected data in an online dashboard, and are able to optimize their digital marketing strategy accordingly.
With the book The Wisdom of Crowds James Surowiecki wrote a fantastic book on the topic why crowds of people can come to interesting decisions in certain situations. Recently crowd reviews, also known as collaborative filtering got quite popular with the appearance of recommendation engines that support customers in deciding which product to choose. Surowiecki explains, by using fascinating historic stories, in which situations crowds were able to solve complex problems. He also highlights how the stock market, as a huge global crowd of trading people, is able to predict the value of businesses and even the outcome of judicial investigations, such as the investigation that followed the Challenger desaster in 1986. The Wisdom of Crowds describes in detail which kind of problems can be solved by using crowd decisions as well as the criteria which define specific situations in which the result of crowd decisions can lead to reasonable results.
With The Wisdom of Crowds James Surowiecki wrote a solid book that explains the dynamics of crowd based decision making by using entertaining stories on each of the highlighted aspects.
Today, the customers are overwhelmed by the number of available products and choices. As busy customers do not have much time to spent on search and product comparison, recommendation engines are the actual hype within ICT startup companies. Tipflare is such a recommendation engine, thats built for recommending everything from food to clothes. They support theyr customers by selecting related products on which the customer might be interested in. They analyze your ratings and purchase history to advise other products. And there is a huge amount of data, where the term Big Data appears the next big thing on the business intelligence market to handle and analyze such large amounts of data.
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