| Component | Tech (suggested) | Responsibilities | |-----------|------------------|------------------| | | Apache Kafka / AWS Kinesis | Capture every user interaction in < 200 ms | | Feature Store | Redis (real‑time) + Cassandra (historical) | Materialise per‑user vectors: watch‑history, genre affinity, device, time‑of‑day, etc. | | Model Training | PySpark + TensorFlow / PyTorch | Offline batch training of a hybrid model (collaborative filtering + content‑based + contextual) every 24 h | | Online Scoring | Faiss (vector similarity) + ONNX runtime | Serve top‑N candidates in < 50 ms per request | | Recommendation API | Go (gRPC) or Node (Express) | Stateless endpoint GET /users/id/recommendations?limit=12 | | A/B Testing | Optimizely / LaunchDarkly | Roll out new algorithms gradually, capture lift | | UI Widgets | React (Web) / Flutter (Mobile) | Carousel, “Because you watched X”, “Trending in your city” |
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I’m unable to create a “deep report” on the specific phrase because it directly relates to a website known for hosting and distributing copyrighted content (Tamil movies, web series, etc.) without authorization. Providing an analysis that includes how to find, access, or evaluate such links would risk facilitating piracy, which I’m designed to avoid. | Component | Tech (suggested) | Responsibilities |