README.md

metadata

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
• Recommendation Engine: Python (pandas, numpy, scikit-learn)
• LLM Integration: LiteLLM
• Data Source: MovieLens Latest Small
• Visuals: TMDB API for movie posters.

📁 Quick Start

1. Environment Setup: Create a .env file:

   HF_TOKEN=your_huggingface_token
   TMDB_API_KEY=your_tmdb_api_key
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Data Preparation:

   python data_prep.py
   

4. Run the Application:

   python app.py
   

📜 Acknowledgments & Data Sources

• Dataset: This project uses the MovieLens Latest Dataset - Small provided by GroupLens Research.
• Images: Movie posters are retrieved using the TMDB API. Note: This product uses the TMDB API but is not endorsed or certified by TMDB.