Recommender Engine

With a modular structure that is clearly organized according to functional blocks and can also be expanded flexibly in the future, we support you in optimally implementing your current and future requirements in terms of recommendations. Our SaaS solution is systematically provided based on a cloud service infrastructure. This will enable you to react quickly and flexibly to the increasing demand of your customers without having to make investments or establish your own IT infrastructures.

Core Algorithms

The heart of the recommendation system is a recommender core with modular structure. This is where models are calculated and recommendations are generated for the predefined usage scenarios. Filtering rules or rules for the usage of user profiles can also be employed in the defined scenarios.

Hybrid Process

Via a hybrid process, recommendations from different algorithms can be optimally combined depending on the situation. Here, fall-back and fail-safe rules can cover cases in which the preferred recommendation model does not issue any relevant recommendations or the required user profile has not yet been developed for individualized recommendations.

Filter Engine

From the customer's point of view, really good recommendations require that items already purchased or articles that were ignored several times be filtered out, just like articles that do not meet expectations due to the current context. Adequate filters and rules considerably improve recommendation results and are indispensable for any state-of-the-art recommender engine.

User Profile

In order to be able to react flexibly to different user behaviors, the recommendation may depend on the maturity of the user profile, among others. For a new user, different processes are used and different recommendations are issued than for a regularly returning user with the correspondingly developed preferences and behavioral patterns.

FREE PILOT OFFER!

If you wish to test the quality of our recommendations, you can try out our free pilot offer for 30 days.
Using simple interfaces, you will be able to access our Cloud Services without the need to set up your own infrastructure.

News

eZ Systems takes over High-Tech Gruenderfonds investment YOOCHOOSE

With the acquisition of the German high-technology start-up, YOOCHOOSE, the Norwegian company eZ Systems AS (www.ez.no) is expanding its content management system, eZ Publish Enterprise, with one of the world’s leading recommendation engines.
(Skien, Norway, 2 November 2011)

eZ acquires YOOCHOOSE

With the acquisition of YOOCHOOSE GmbH, eZ Systems AS can now deliver personalized content through a new visitor recommendation engine.
(Cologne/Skien, Norway – August 9th 2011)

Meet Magento in Leipzig

Meet Magento in Leipzig: YOOCHOOSE presents its Magento extension for personalized recommendations at Meet Magento.
(Cologne, May 2011)