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dmckinney-ml/movie-recommender-artifacts

🎬 Two-Tower + FAISS + XGBoost Recommender — Inference Artifacts This dataset contains the serialized inference artifacts for the Two-Stage Movie Recommendation System built using TensorFlow Recommenders, FAISS, and XGBoost. These artifacts support low-latency retrieval and ranking for the deployed Hugging Face Space: 🔗 Space: two-tower-faiss-xgb-recommender 📌 Overview The recommendation system follows a two-stage architecture: Retrieval (Stage 1)… See the full description on the dataset page: https://huggingface.co/datasets/dmckinney-ml/movie-recommender-artifacts.

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Dataset Card

🎬 Two-Tower + FAISS + XGBoost Recommender — Inference Artifacts

This dataset contains the serialized inference artifacts for the Two-Stage Movie Recommendation System built using TensorFlow Recommenders, FAISS, and XGBoost.

These artifacts support low-latency retrieval and ranking for the deployed Hugging Face Space:

🔗 Space: two-tower-faiss-xgb-recommender

📌 Overview

The recommendation system follows a two-stage architecture:

Retrieval (Stage 1)

  • —Two-tower neural embedding model (TensorFlow Recommenders)
  • —Vector similarity search using FAISS (IndexFlatIP)
  • —Cosine similarity implemented via inner-product normalization

Ranking (Stage 2)

  • —XGBoost learning-to-rank model
  • —Input features:
  • —User embedding
  • —Movie embedding
  • —Genre one-hot features
Artifacts are structured for inference-only usage.

🗂️ Artifact Contents

FileDescription
faiss.indexFAISS index containing 87K+ movie embeddings
xgb_ranker.jsonTrained XGBoost ranking model
two_tower/user_model/Saved user embedding sub-model
two_tower/movie_model/Saved movie embedding sub-model
two_tower/genre_model/Saved genre projection model
two_tower/rating_model/Saved rating prediction head
movie_id_to_row.jsonMapping from MovieLens ID → FAISS index row
movies.parquetMovie metadata (title + features)

📊 Training Data

Models were trained on the MovieLens 32M dataset:

  • —32 million ratings
  • —~87,585 movies
  • —~200,948 users

Preprocessing performed using:

  • —Apache Beam (Cloud Dataflow)
  • —BigQuery
  • —Kubeflow Pipelines (Vertex AI)

🧠 Embedding Details

PropertyValue
Embedding dimensionalityTunable (selected via Hyperband)
Vector spaceNormalized for cosine similarity
FAISS index typeIndexFlatIP
Total indexed items~87K

📈 Ranking Model

PropertyValue
XGBoost objectiverank:pairwise
Evaluation metricNDCG
Hyperparameter tuningOptuna (50 trials)
Validation strategyUser-level cold-start split

🚀 Intended Usage

These artifacts are designed for:

  • —✅ Inference inside the Hugging Face Space
  • —✅ Reproducible local experimentation
  • —✅ Demonstration of production-style ML architecture
⚠️ Not intended for retraining without the full GCP pipeline.

🛠️ Loading Example

python
import faiss
import xgboost as xgb
import tensorflow as tf

# FAISS index
index = faiss.read_index("faiss.index")

# XGBoost ranker
booster = xgb.Booster()
booster.load_model("xgb_ranker.json")

# Two-tower sub-models
user_model = tf.keras.models.load_model("two_tower/user_model")
movie_model = tf.keras.models.load_model("two_tower/movie_model")

📄 License