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---
title: Recommendation LLM Explainer
emoji: 🎬
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.3.0
app_file: app.py
pinned: false
---
# AI-Powered Explainable Recommendations
This project is a high-fidelity demonstration of how Large Language Models (LLMs) can bridge the "explainability gap" in recommendation systems. It transforms opaque collaborative filtering data into transparent, persona-aligned narratives.
## 🚀 Overview
Instead of providing a "black box" list of suggestions, this system uses LLMs to synthesize natural language explanations based on user personas and collaborative filtering logic.
### Key Features
- **Specialist Centroids**: Personas built by aggregating top "specialists" for specific genres.
- **Layered Re-ranking**: Incorporates Genre Affinity Boosts and Recency Bias.
- **Narrative Synthesis**: Uses LiteLLM to generate 3-sentence explanations in "Logic-Driven" or "Social-Behavioral" styles.
## 🛠 Tech Stack
- **Interface**: [Gradio](https://gradio.app/)
- **Recommendation Engine**: Python (`pandas`, `numpy`, `scikit-learn`)
- **LLM Integration**: [LiteLLM](https://github.com/BerriAI/litellm)
- **Data Source**: [MovieLens Latest Small](https://grouplens.org/datasets/movielens/latest/)
- **Visuals**: [TMDB API](https://www.themoviedb.org/documentation/api) for movie posters.
## 📁 Quick Start
1. **Environment Setup**:
Create a `.env` file:
```env
HF_TOKEN=your_huggingface_token
TMDB_API_KEY=your_tmdb_api_key
```
2. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
3. **Data Preparation**:
```bash
python data_prep.py
```
4. **Run the Application**:
```bash
python app.py
```
## 📜 Acknowledgments & Data Sources
- **Dataset**: This project uses the **MovieLens Latest Dataset - Small** provided by [GroupLens Research](https://grouplens.org/datasets/movielens/).
- **Images**: Movie posters are retrieved using the **TMDB API**. *Note: This product uses the TMDB API but is not endorsed or certified by TMDB.*
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