Recommendations / Personalization

Recommendation Systems

Personalized selection based on data analysis and user preferences

Recommendation Systems

Description

The system selects the most suitable option based on preference analysis, similar user behavior, and item characteristics. With feedback, recommendations become more accurate with each interaction. Applicable to any objects: documents, products, technical parameters, or precedents.

Typical Tasks

  • Product and content selection based on interaction history
  • Similar document and precedent recommendations
  • Interface and output personalization for each user
  • Optimal technical parameter and configuration selection

Technologies

Collaborative Filtering Content-Based Matrix Factorization PyTorch LightFM Redis A/B testing

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