Recommender Systems

Recommender systems are time-critical, as the recommendations for a customer must be available without delay to ensure a seamless experience. This is the case with online shopping, for example. When the user adds an item to their shopping cart, further product suggestions should be displayed immediately. Users do not wait for these suggestions. If they don’t come immediately, they move on and the opportunity to sell more products is lost.

A typical use-case for recommender systems are shopping websites. For example, when a customer adds an item to their cart, you want to quickly propose other items, which could also be interesting to them. Showing the recommendations is time-critical, because the customer would either leave or proceed with the checkout otherwise.

On the other hand, there is usually a high volume of customers that the recommender system is used for. This can lead to high server costs, especially when scaling the service. To prevent costs from exploding, resource-efficient deployment of the underlying models is essential.

Recommender systems are often used to determine the next item to present to the customer. This includes recommendations about which music piece, movie or clip to play next. Users expect to get such recommendations right when they open the current item. In order to meet these requirements, you have to have the right deployment concept for your recommender system.

Deployment done right

Deploying artificial intelligence / machine learning models the correct way is easy with our tools. Just define your models in our web-app, find the right server architecture, and deploy your models with a single click on-premise or to the cloud. Our all-in-one solution eliminates the hassle, optimizing both your models and deployment configurations to deliver maximum inference speed while minimizing server costs.

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