Recommendation is more than algorithm, the key to recommendation is data.

Data is the foundation of all algorithm, the basis of all iteration!

Full-process Recommendation System of "Data + Intelligence + Feedback"

Sensors recommender system is a full-process closed-loop recommendation scheme. It collects full-end data (including app, web, applet, H5, server, business data, third-party data, etc.) and uses deep learning and semantic analysis model to form the recommendation engine. The results can be analyzed in real-time with instant feedback and accurate algorithm iteration.

The Technical Architecture of Sensors Recommender

The Advantages of Sensors Recommender

  • Powerful Algorithm Function

    Provide advanced machine learning algorithms, such as deep learning to ensure excellent recommendation effect.

  • Privatized Deployment

    Enable privatized deployment, ensure data security and secondary development of algorithm model.

  • Manual Intervention

    Provide a visualized platform that gets the recommendation results in line with actual business demands.

  • Real-time Recommendation

    Full-end data collection, algorithm model calculation, and recommendation results shown are all performed in real-time to satisfy high effectiveness and improve user experience.

  • “White Box” Algorithm

    Provide overall solutions to support your algorithm team. Enable secondary development with algorithm modules and results that could be flexibly transferred.

  • Accurate Algorithm Iteration

    Flexibly adjust feature sets, iterate model parameters, and adjust possible solutions to meet business modifications to get higher returns.

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