2026 movies from The Movie Database (TMDB) provide plot overviews and genre labels. The dataset is designed for building and testing recommendation engines. It was compiled by an unknown author and shared on Kaggle.
Use Cases
- Train a content-based recommender to suggest movies by calculating text similarity between plot_summary columns.
- Build a genre classification model using plot_overview text features to predict genre labels.
- Create a hybrid filtering system by combining user ratings with latent features extracted from movie plot text.
- Analyze genre trends and plot narrative structures over time using the provided genre tags and text data.
Strengths
- Contains data for 2026 distinct movies.
- Includes structured text features (plot overviews) and categorical labels (genres).
Limitations
- Unknown row count limits statistical power for certain modeling tasks.
- Potential geographic or language bias if sourced primarily from a single database like TMDB.
- Data freshness is unknown, which may affect relevance for current movie recommendations.
Provenance
- Source
- The Movie Database (TMDB).
- Collection Method
- Aggregated from TMDB's public API or database exports.