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Movie-Recommendation-System / docs /DATASETS.md

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# Dataset Strategy

The legacy project uses an older IMDb-style metadata CSV. V2 keeps that as a fallback, but the main dataset path is now:

```text
data/processed/movie_catalog.csv
```

The app automatically prefers that processed catalog when it exists. Otherwise, it falls back to:

```text
data/legacy/main_data.csv
```

Legacy CSVs from the original project live under:

```text
data/legacy/
```

## TMDb Key

Use this exact name locally and on Hugging Face:

```text
TMDB_API_KEY
```

Local `.env` example:

```bash
TMDB_API_KEY=your_tmdb_api_key_here
MOVIE_CATALOG_PATH=data/processed/movie_catalog.csv
```

On Hugging Face Spaces, add a repository secret named `TMDB_API_KEY`.

This project uses the official TMDb API. It does not scrape TMDb pages.

## MovieLens Variants

Use `small` for fast development:

```bash
python scripts/prepare_movielens.py --variant small
```

Recommended first deployment pipeline:

```bash
make pipeline-demo
```

This combines MovieLens small ratings with a broad recent TMDb catalog.

Use `32m` for the serious/offline project dataset:

```bash
python scripts/prepare_movielens.py --variant 32m
```

The `32m` raw dataset is too large to treat casually inside the repo. Generate it offline, keep `data/raw/` untracked, and commit or upload only compact processed artifacts that the Hugging Face Space needs.

## Filtering Examples

Build a compact catalog with only movies that have at least 20 ratings:

```bash
python scripts/prepare_movielens.py --variant 32m --min-ratings 20
```

Build a tiny demo catalog:

```bash
python scripts/prepare_movielens.py --variant small --limit 2000
```

The generated files are:

```text
data/processed/movie_catalog.csv
data/processed/catalog_metrics.csv
```

## Recent Movie Coverage With TMDb

MovieLens gives us ratings. TMDb gives us current movie discovery. To add post-2019 catalog coverage:

```bash
python scripts/build_tmdb_catalog.py --min-year 2019 --pages 50
```

This writes:

```text
data/processed/tmdb_recent_catalog.csv
```

This is not meant to be exhaustive. It pulls popular recent movies, which is better for a responsive demo than ingesting every low-vote or obscure title.

Country/origin metadata is enriched from TMDb details for a representative subset of the broad recent catalog. The front-page origin note should be read as a transparency estimate, not a census of every title.

For broader 2018-2026 coverage, run a per-year sweep:

```bash
make tmdb-broad-recent
```

That writes:

```text
data/processed/tmdb_broad_recent_catalog.csv
```

Use the broad catalog when recall matters more than artifact size and curation quality.

To keep both MovieLens and TMDb rows, write MovieLens to a separate file and merge:

```bash
python scripts/prepare_movielens.py \
  --variant 32m \
  --min-ratings 20 \
  --catalog-output-path data/processed/movielens_32m_catalog.csv
python scripts/build_tmdb_catalog.py \
  --min-year 2019 \
  --pages 50
python scripts/merge_catalogs.py \
  --inputs data/processed/movielens_32m_catalog.csv data/processed/tmdb_recent_catalog.csv \
  --output-path data/processed/movie_catalog.csv
```

## Collaborative Filtering Baseline

After preparing MovieLens, build item-item collaborative neighbors:

```bash
python scripts/train_item_cf.py \
  --ratings-path data/raw/ml-latest-small/ratings.csv \
  --catalog-path data/processed/movie_catalog.csv
```

For a larger `32m` run, start with conservative filters:

```bash
python scripts/train_item_cf.py \
  --ratings-path data/raw/ml-32m/ratings.csv \
  --catalog-path data/processed/movie_catalog.csv \
  --min-item-likes 20 \
  --max-users 100000
```

The generated files are:

```text
data/processed/item_neighbors.csv
data/processed/item_cf_metrics.csv
data/processed/item_cf_eval.csv
data/processed/semantic_index.npz
data/processed/ranking_eval.csv
data/processed/hybrid_weights.json
```

When `item_neighbors.csv` exists, the FastAPI app automatically blends collaborative recommendations with the content baseline.

Evaluate the baseline:

```bash
python scripts/evaluate_item_cf.py \
  --ratings-path data/raw/ml-latest-small/ratings.csv \
  --neighbors-path data/processed/item_neighbors.csv \
  --k 10
```

Build the semantic retrieval artifact:

```bash
python scripts/build_semantic_index.py \
  --catalog-path data/processed/movie_catalog.csv \
  --output-path data/processed/semantic_index.npz
```

Install `requirements-ml.txt` before this offline build. The default builder uses `sentence-transformers/all-MiniLM-L6-v2`; the live Hugging Face Space loads the resulting artifact directly and does not download or load an embedding model during startup.

Evaluate the four retrieval ablations and select hybrid weights only on validation data:

```bash
python scripts/evaluate_recommenders.py \
  --ratings-path data/raw/ml-latest-small/ratings.csv \
  --catalog-path data/processed/movie_catalog.csv \
  --semantic-path data/processed/semantic_index.npz
```

The evaluator reserves each user's two most recent liked movies for validation and test, then uses the immediately preceding liked movie as the recommendation seed so it matches the app's selected-movie flow. Collaborative interactions for both held-out movies are excluded from training, avoiding the leakage in an evaluation that scores a graph trained on all ratings.

## Why TMDb Is Still Needed

MovieLens is ideal for ratings and collaborative filtering, but it is not a poster/cast/overview database. TMDb gives the app current presentation metadata and lets the UI stay polished.

The long-term target is:

```text
MovieLens ratings -> collaborative filtering
MovieLens/TMDb metadata -> content features
TMDb API -> posters, cast, current movie details
```

	# Dataset Strategy

	The legacy project uses an older IMDb-style metadata CSV. V2 keeps that as a fallback, but the main dataset path is now:

	```text
	data/processed/movie_catalog.csv
	```

	The app automatically prefers that processed catalog when it exists. Otherwise, it falls back to:

	```text
	data/legacy/main_data.csv
	```

	Legacy CSVs from the original project live under:

	```text
	data/legacy/
	```

	## TMDb Key

	Use this exact name locally and on Hugging Face:

	```text
	TMDB_API_KEY
	```

	Local `.env` example:

	```bash
	TMDB_API_KEY=your_tmdb_api_key_here
	MOVIE_CATALOG_PATH=data/processed/movie_catalog.csv
	```

	On Hugging Face Spaces, add a repository secret named `TMDB_API_KEY`.

	This project uses the official TMDb API. It does not scrape TMDb pages.

	## MovieLens Variants

	Use `small` for fast development:

	```bash
	python scripts/prepare_movielens.py --variant small
	```

	Recommended first deployment pipeline:

	```bash
	make pipeline-demo
	```

	This combines MovieLens small ratings with a broad recent TMDb catalog.

	Use `32m` for the serious/offline project dataset:

	```bash
	python scripts/prepare_movielens.py --variant 32m
	```

	The `32m` raw dataset is too large to treat casually inside the repo. Generate it offline, keep `data/raw/` untracked, and commit or upload only compact processed artifacts that the Hugging Face Space needs.

	## Filtering Examples

	Build a compact catalog with only movies that have at least 20 ratings:

	```bash
	python scripts/prepare_movielens.py --variant 32m --min-ratings 20
	```

	Build a tiny demo catalog:

	```bash
	python scripts/prepare_movielens.py --variant small --limit 2000
	```

	The generated files are:

	```text
	data/processed/movie_catalog.csv
	data/processed/catalog_metrics.csv
	```

	## Recent Movie Coverage With TMDb

	MovieLens gives us ratings. TMDb gives us current movie discovery. To add post-2019 catalog coverage:

	```bash
	python scripts/build_tmdb_catalog.py --min-year 2019 --pages 50
	```

	This writes:

	```text
	data/processed/tmdb_recent_catalog.csv
	```

	This is not meant to be exhaustive. It pulls popular recent movies, which is better for a responsive demo than ingesting every low-vote or obscure title.

	Country/origin metadata is enriched from TMDb details for a representative subset of the broad recent catalog. The front-page origin note should be read as a transparency estimate, not a census of every title.

	For broader 2018-2026 coverage, run a per-year sweep:

	```bash
	make tmdb-broad-recent
	```

	That writes:

	```text
	data/processed/tmdb_broad_recent_catalog.csv
	```

	Use the broad catalog when recall matters more than artifact size and curation quality.

	To keep both MovieLens and TMDb rows, write MovieLens to a separate file and merge:

	```bash
	python scripts/prepare_movielens.py \
	--variant 32m \
	--min-ratings 20 \
	--catalog-output-path data/processed/movielens_32m_catalog.csv

	python scripts/build_tmdb_catalog.py \
	--min-year 2019 \
	--pages 50

	python scripts/merge_catalogs.py \
	--inputs data/processed/movielens_32m_catalog.csv data/processed/tmdb_recent_catalog.csv \
	--output-path data/processed/movie_catalog.csv
	```

	## Collaborative Filtering Baseline

	After preparing MovieLens, build item-item collaborative neighbors:

	```bash
	python scripts/train_item_cf.py \
	--ratings-path data/raw/ml-latest-small/ratings.csv \
	--catalog-path data/processed/movie_catalog.csv
	```

	For a larger `32m` run, start with conservative filters:

	```bash
	python scripts/train_item_cf.py \
	--ratings-path data/raw/ml-32m/ratings.csv \
	--catalog-path data/processed/movie_catalog.csv \
	--min-item-likes 20 \
	--max-users 100000
	```

	The generated files are:

	```text
	data/processed/item_neighbors.csv
	data/processed/item_cf_metrics.csv
	data/processed/item_cf_eval.csv
	data/processed/semantic_index.npz
	data/processed/ranking_eval.csv
	data/processed/hybrid_weights.json
	```

	When `item_neighbors.csv` exists, the FastAPI app automatically blends collaborative recommendations with the content baseline.

	Evaluate the baseline:

	```bash
	python scripts/evaluate_item_cf.py \
	--ratings-path data/raw/ml-latest-small/ratings.csv \
	--neighbors-path data/processed/item_neighbors.csv \
	--k 10
	```

	Build the semantic retrieval artifact:

	```bash
	python scripts/build_semantic_index.py \
	--catalog-path data/processed/movie_catalog.csv \
	--output-path data/processed/semantic_index.npz
	```

	Install `requirements-ml.txt` before this offline build. The default builder uses `sentence-transformers/all-MiniLM-L6-v2`; the live Hugging Face Space loads the resulting artifact directly and does not download or load an embedding model during startup.

	Evaluate the four retrieval ablations and select hybrid weights only on validation data:

	```bash
	python scripts/evaluate_recommenders.py \
	--ratings-path data/raw/ml-latest-small/ratings.csv \
	--catalog-path data/processed/movie_catalog.csv \
	--semantic-path data/processed/semantic_index.npz
	```

	The evaluator reserves each user's two most recent liked movies for validation and test, then uses the immediately preceding liked movie as the recommendation seed so it matches the app's selected-movie flow. Collaborative interactions for both held-out movies are excluded from training, avoiding the leakage in an evaluation that scores a graph trained on all ratings.

	## Why TMDb Is Still Needed

	MovieLens is ideal for ratings and collaborative filtering, but it is not a poster/cast/overview database. TMDb gives the app current presentation metadata and lets the UI stay polished.

	The long-term target is:

	```text
	MovieLens ratings -> collaborative filtering
	MovieLens/TMDb metadata -> content features
	TMDb API -> posters, cast, current movie details
	```