OmniFlix - Unified Cross-Domain Recommendation System

Hybrid recommendation architecture unifying IMDb, TMDB, and MyAnimeList datasets into a multi-domain discovery engine using LightFM hybrid collaborative filtering and TF-IDF content similarity.

Algorithm:LightFM + TF-IDF (WARP Loss)HYBRID_ML

Matrix Sparsity:99.82% Sparse Coo-MatrixOPTIMIZED

// REAL-TIME PERFORMANCE METRICSREFRESH_RATE: 1000ms

DATA_SOURCES_MERGEDLIVE_TELEMETRY

0domains

IMDb, TMDB, and MyAnimeList schemas harmonized

CATALOG_RECORDSLIVE_TELEMETRY

0k+

Cleaned, deduplicated multimedia items

INFERENCE_P99LIVE_TELEMETRY

0ms

Sub-50ms recommendation response time

RECOMMENDATION_HITRATELIVE_TELEMETRY

Evaluated on Top-10 recall benchmarks

// TOPOLOGICAL PIPELINE MAP

SYSTEM_PIPELINE_ARCHITECTURE_GRAPH[OMNIFLIX_TOPOLOGY]

NODES: ONLINEFLOW: DUPLEX_STREAM

01 // INGESTIONSTEP_1

→IMDb Official TSV Dataset Dumps
→TMDB API Real-Time Metadata Synchronization
→MyAnimeList Comprehensive Profile Scrapers
→Cross-Domain Entity Disambiguation Pipeline

SENSOR / INFLOWSTANDBY

02 // INFERENCE & PROCESSINGSTEP_2

→LightFM Hybrid Matrix Factorization (WARP Loss)
→TF-IDF Vectorized Item Attribute Embeddings
→Cosine Similarity In-Memory Matrix Index
→Cold-Start Fallback Ranking Engine

ACCELERATED_COREACTIVE_NODE

03 // TRANSPORT & BUSSTEP_3

→FastAPI High-Throughput REST Gateway
→Redis High-Speed Recommendations Cache
→Async Batch Score Evaluator
→Dockerized Scalable Deployment Pods

LOW_LATENCYSTANDBY

04 // CLIENT & INTERFACESSTEP_4

→Unified Web Discovery Interface
→Cross-Domain Attribute Weight Inspector
→Interactive Latent Factor Explorer

TELEMETRY_OUTSTANDBY

DATA_BUS:G.711u / WebSocket / Async Event Loop

CLICK_NODE_TO_INSPECT_VECTOR_STATE

$ cat /etc/manifests/omniflix.json

ENCODING: UTF-8

{
  "project_id": "omniflix",
  "status": "DEPLOYED",
  "timeframe": "2025",
  "pipeline_stages": {
    "ingestion": [
      "IMDb Official TSV Dataset Dumps",
      "TMDB API Real-Time Metadata Synchronization",
      "MyAnimeList Comprehensive Profile Scrapers",
      "Cross-Domain Entity Disambiguation Pipeline"
    ],
    "inference": [
      "LightFM Hybrid Matrix Factorization (WARP Loss)",
      "TF-IDF Vectorized Item Attribute Embeddings",
      "Cosine Similarity In-Memory Matrix Index",
      "Cold-Start Fallback Ranking Engine"
    ],
    "transport": [
      "FastAPI High-Throughput REST Gateway",
      "Redis High-Speed Recommendations Cache",
      "Async Batch Score Evaluator",
      "Dockerized Scalable Deployment Pods"
    ],
    "client": [
      "Unified Web Discovery Interface",
      "Cross-Domain Attribute Weight Inspector",
      "Interactive Latent Factor Explorer"
    ]
  },
  "telemetry": {
    "DATA_SOURCES_MERGED": "3domains",
    "CATALOG_RECORDS": "140k+",
    "INFERENCE_P99": "42ms",
    "RECOMMENDATION_HITRATE": "89%"
  }
}

JOURNALCTL_OUTPUT_LOGS :: OMNIFLIX

LEVEL: INFO / DIAGNOSTIC

01[2025-10-18 16:30:12.110] DATA_INGEST: Merging 142k items across Cinema, TV, and Anime taxonomies

02[2025-10-18 16:30:15.340] ENTITY_RESOLVE: Resolved 99.4% cross-platform title collisions

03[2025-10-18 16:30:18.890] MATRIX_BUILD: Constructed 140k x 85k user-item interaction sparse matrix

04[2025-10-18 16:30:20.450] MODEL_TRAIN: LightFM WARP optimization converged with precision@k = 0.892

05[2025-10-18 16:30:22.010] REDIS_CACHE: Pre-warmed top recommendations cluster (Latency: 12ms)

06[2025-10-18 16:30:24.000] HYBRID_ENGINE: Operational for cross-domain queries [STATUS: HEALTHY]

// SUBSYSTEM ENGINEERING SPECIFICATIONS

>1. Resolving Multi-Source Schema Sparsity

▪Developed custom normalization mappings across diverse rating scales (IMDb 1-10, TMDB percentage, MAL 1-10).
▪Used LightFM hybrid factorization to incorporate metadata item features alongside interaction data, overcoming cold-start problems.

← Return to System DashboardTERMINAL_NODE: 0xomniflix [STABLE]