Revise hybrid recommender system design document

[emnl51]

Motivation

• Provide a production-ready, end-to-end design for a hybrid movie & series recommender that combines explicit/implicit signals, content, metadata and session context to balance accuracy, latency and scalability.
• Clarify pipeline, model responsibilities, fusion strategy and operational decisions so engineering teams can implement MVP → production increments quickly.

Description

• Added RECOMMENDER_SYSTEM_DESIGN.md containing an architecture diagram, a step-by-step data pipeline, and model layer roles for content-based, collaborative filtering, rule-based and deep sequence models.
• Specified a late-fusion scoring formula score(u,i) = w_cf*s_cf + w_content*s_content + w_seq*s_seq + w_rule*s_rule, model registry/A-B/versioning guidance, and a simple online pseudocode recommend(...) with reranking.
• Included technology recommendations (TF-IDF, Sentence-BERT, ALS, LightFM, TFR/Transformers4Rec), evaluation metrics (Precision@K, NDCG@K, CTR, watch-time), API example (GET /recommendations?user_id=123&limit=20) and real-time streaming / caching patterns (Kafka/Redis, batch inference, Redis cache).
• Added MVP → Production roadmap and a risk/mitigation section covering cold-start, sparsity, filter bubble and explainability trade-offs.

Testing

• No automated tests were executed because this is a documentation-only change and contains design guidance rather than runnable code.

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