How does the Trendskout AI recommendation engine work?

Recommendations are natively supported by the Trendskout AI platform. It uses a recommendation engine to suggest hyper-personalised services, content or products. Recommendation engines can be supported by different types of classification and clustering algorithms, which are implemented to support a particular service or product.

They often rely on metric functions, which define the distance between one data point (e.g. a website visitor) and another nearest data point (e.g. the most relevant blog post).

Application Examples

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In addition to specific modules for media companies, the Trendskout AutoML platform also offers many possibilities for IT teams to quickly set up AI applications, Powered by our unique double AI low and easy to use UI that fully automates data transformations, algorithm selection and hypertuning.

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Recommendation is one of the available Trendskout AI Flow analysis-functions.



How does this work technically?


Recommendation engine algorithms require little or no training data, so they are unsupervised algorithms. The value that must be “recommended” is taken from the nearest data point. This means that the distance function used to calculate this point is a crucial part. As with other AI and Deep Learning algorithms, various functions and configurations are possible, whether or not combined with additional clustering or classification algorithms. The evaluation of these combinations is carried out independently by Trendskout Auto ML .

Recommendation vs Classification

Recommendation engines are used when there are many different types of content, products or services that are eligible to present. If this number is limited, a Classification analysis is more appropriate, for which a training phase is required.

Recommendation + Trend skout

Recommendation algorithms are not a separate set of algorithms but are combinations of AI and Deep Learning techniques. This means that a large number of different algorithm configurations, each with their specific hyper-tuning, is possible. Trendskout performs the selection of these algorithms and their hyper-tuning independently, and offers recommendation as ready-to-use AI function.

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