<aside> <img src="https://s3-us-west-2.amazonaws.com/secure.notion-static.com/66809a2a-6764-4eec-9ba5-66e66cf5559d/rumoicone.png" alt="https://s3-us-west-2.amazonaws.com/secure.notion-static.com/66809a2a-6764-4eec-9ba5-66e66cf5559d/rumoicone.png" width="40px" /> Interactions Ranking is a powerful feature designed to enhance the precision of user recommendations. Easily adjust rules via our intuitive dashboard, ensuring personalized user experiences in tune with our business goals.

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Key Benefits

<aside> <img src="/icons/bullseye_pink.svg" alt="/icons/bullseye_pink.svg" width="40px" /> Customized Interaction Ranking

Define the priority of interactions based on your unique business needs.

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<aside> <img src="/icons/bullseye_pink.svg" alt="/icons/bullseye_pink.svg" width="40px" /> Enhanced Recommendation Precision

Fine-tune recommendation algorithms to reflect the significance of different user actions.

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<aside> <img src="/icons/bullseye_pink.svg" alt="/icons/bullseye_pink.svg" width="40px" /> Flexible Configuration

Adapt interaction ranking based on evolving business needs and user behaviors.

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What is a user interaction?

What we call an interaction corresponds to the actions a user can perform on your platform. For instance, a user can click on an article, he can share a film, he can like a music.

Rumo allows the following interactions to map to your platform.

Untitled Database

<aside> <img src="https://s3-us-west-2.amazonaws.com/secure.notion-static.com/a8ce9931-8336-4e15-88ec-21c924d764bd/favicon-blue-1.png" alt="https://s3-us-west-2.amazonaws.com/secure.notion-static.com/a8ce9931-8336-4e15-88ec-21c924d764bd/favicon-blue-1.png" width="40px" /> See API Doc | Submit user interactions

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Define Interaction Ranking

In our system, each interaction carries a default ranking, influencing personalized recommendations. For example, click on an item typically carries less weight (๐Ÿ”บ) than hitting like (๐Ÿ”บ๐Ÿ”บ๐Ÿ”บ๐Ÿ”บ๐Ÿ”บ) on an item in our algorithm calculations. However, hitting dislike (๐Ÿ”ป๐Ÿ”ป) will carry a negative weight in our algorithm computation, in order to take into account the user tasteโ€™s feedback in the recommendation engine.