From raw score to visible suggestion: how to turn latent vectors into highly relevant personalised recommendations. Discover the mechanics of scoring, ranking and real-time generation.
Simulate the recommendation engine for Marie after training the SVD + Neural Collaborative Filtering model
Marie liked Inception (5⭐), Interstellar (4⭐), Dune (4.5⭐). The system predicts she will love Blade Runner 2049, with a 91% probability of rating it ≥ 4.
Based on: high similarity with Inception + 2001: A Space Odyssey (via SVD + user-based CF)
Hybrid score: content (epic science fiction) + collaborative filtering
Strong latent correlation with her ratings of Nolan & Villeneuve films
Neural CF + item cosine similarity — a very good thematic match
Exploratory recommendation (diversity) with medium confidence