Scenario: An e-commerce company aims to provide personalized recommendations to users in real-time. How would you design a real-time recommendation engine, and what factors would you consider to ensure accuracy and efficiency?
- Collaborative filtering algorithms, Apache Spark for data processing, Redis for caching, RESTful APIs for serving recommendations
- Content-based filtering methods, Apache Storm for stream processing, MongoDB for storing user preferences, SOAP APIs for serving recommendations
- Matrix factorization algorithms, Apache NiFi for data ingestion, Elasticsearch for indexing, gRPC for serving recommendations
- Singular Value Decomposition (SVD) techniques, Apache Flink for data processing, Memcached for caching, GraphQL for serving recommendations
Designing a real-time recommendation engine for an e-commerce company involves employing collaborative filtering algorithms to analyze user behavior and preferences. Apache Spark facilitates data processing to generate personalized recommendations, with Redis caching frequently accessed items for faster retrieval. RESTful APIs ensure seamless integration with the e-commerce platform for serving recommendations to users in real-time.
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