InBeat - Interest Beat

Interest Beat is a service for recommendation of content. InBeat was designed with emphasis on versatility, scalability and extensibility. The core contains the General Analytics INterceptor module, which collects and aggregates user interactions, the Preference Learning module and the Recommender System module.

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New Jaroslav Kuchař, Tomáš Kliegr, InBeat: JavaScript recommender system supporting sensor input and linked data, Knowledge-Based Systems, Volume 135, 1 November 2017, Pages 40-43, ISSN 0950-7051,

New The InBeat ( recommender developed with support from LinkedTV received a runner-up prize in the News Recommender Challenge (, which focused on recommending news articles in real-time. InBeat came second out of 17 participating systems. During the three weeks of the contest, it handled over 20 million recommendation requests. The News Recommender Challenge is organized in conjunction with the ACM Conference on Recommender Systems ( InBeat will be presented (together with the LinkedTV-supported GAIN platform) also at the main conference (


GAIN (General Analytics INterceptor) collects and aggregates user interactions.

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Preference Learning

This component is responsible for learning the preferences and wraps the underlying learning stack.

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Recommeder System

Provides recommendations of content based on different algorithms.

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If you are already using InBeat then please cite our work!