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Personalization Using Vector Search Workshop

Live Workshops
A powerful, AI-driven search and discovery platform for sports videos, built with Azure AI Search, .NET 8, and Next.js—delivering semantic understanding, vector-based personalization, time-decay recommendations, and balanced feeds that evolve with every user interaction, just like the world's leading media platforms.

What If Your Sports Video Discovery Was Truly Intelligent and Personalized?

  • Most platforms stop at basic keyword matching.
  • Pro sports media apps don’t.
They understand intent ("thrilling overtime winners"), adapt to your viewing history, and blend familiar favorites with exciting new discoveries—keeping fans hooked for hours. In this comprehensive solution showcase, you’ll explore SearchS5: a production-grade, AI-powered sports video platform that delivers exactly that level of sophistication.

What You’ll Build and Learn

This end-to-end solution isn’t a simple demo—it’s a fully deployable system you can study, extend, or adapt:
  • Azure AI Search for scalable full-text, semantic, and vector retrieval
  • Azure OpenAI embeddings (3072-dim) for deep content understanding
  • .NET 8 backend for robust personalization logic and API orchestration
  • Next.js 15 frontend for responsive, locale-aware user experiences
  • Time-decay personalization that evolves with every interaction
  • Hybrid feed curation preventing echo chambers while boosting engagement
The outcome? A professional discovery platform handling videos with sub-second latency—the same patterns driving high-traffic media apps on Azure.

Why This Matters for Your Career

  • Vector-based personalization, hybrid semantic search, and adaptive ranking are foundational to today’s content platforms—whether media streaming, e-commerce product discovery, or knowledge bases. Developers who master integrating Azure AI Search with embeddings, building decay-based recommendation engines, and delivering seamless full-stack experiences are the ones creating addictive, high-engagement products.
  • Semantic + Vector Hybrid Search: Understands queries like "epic comeback goals" even without exact matches.
  • Dynamic User Profiling: Aggregates interactions into a evolving preference vector using exponential time decay and rank weighting.
  • Balanced Curation: 3:1 personalized-to-discovery ratio ensures freshness and variety.
80%+
Of Sports Fans Abandon Apps with Poor Search & Discovery
90%+
Azure AI Search Powers Enterprise-Grade Retrieval Experiences
30–50%
Higher Retention with Personalized, Time-Aware Recommendations

100%

Cloud-Native, Scalable Architecture on Azure