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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+Here’s a draft for a model card that you can use for Hugging Face, detailing the purpose, training data, architecture, and intended use of your recommendation model:
+
+---
+
+# Model Card: Profile-Based Movie Recommendation Model
+
+## Model Overview
+This model is a **profile-based movie recommendation system** designed to recommend movies based on user demographics and genre preferences. It was trained on the [MovieLens 1M dataset](http://files.grouplens.org/datasets/movielens/ml-1m.zip) and uses demographic and genre preferences to create user profiles through clustering. By leveraging user profiles and movie embeddings, the model provides movie recommendations tailored to each user’s interests.
+
+## Model Architecture
+The model is built using **TensorFlow** and **Keras** and employs an **embedding-based architecture**:
+1. **User Profiles and Clustering**: User demographics and genre preferences are clustered into a specified number of profiles using **KMeans** clustering. This results in profile IDs that capture user similarities based on age, occupation, gender, and preferred movie genres.
+2. **Embedding Layers**: 
+   - The **user profile IDs** are embedded in a lower-dimensional space using a trainable embedding layer.
+   - Similarly, **movie IDs** are embedded into a separate lower-dimensional space.
+3. **Dot Product for Recommendation**: The model computes the dot product between the profile embedding and movie embedding, resulting in a similarity score. The higher the score, the more relevant the movie is predicted to be for the user profile.
+
+## Training Dataset
+The model was trained on the [MovieLens 1M dataset](http://files.grouplens.org/datasets/movielens/ml-1m.zip) by GroupLens. The dataset contains **1 million ratings** from **6,040 users** on **3,900 movies**. 
+
+- **Users**: Contains demographic information such as age, gender, and occupation.
+- **Ratings**: Provides ratings from users for different movies.
+- **Movies**: Includes movie titles and genres (e.g., Action, Comedy, Romance).
+
+### Dataset Preparation
+- **Preprocessing**: 
+   - User demographic data was one-hot encoded to include age, occupation, and gender.
+   - User genre preferences were extracted by identifying each user's top-rated genres, with genres being split and exploded for individual assignment.
+- **Clustering**: User profiles were clustered into 10 groups using KMeans clustering based on demographic and genre features.
+- **Embedding Preparation**: Profile IDs and Movie IDs were prepared for embedding layers.
+
+## Training Configuration
+- **Optimizer**: Adam
+- **Loss Function**: Mean Squared Error (MSE)
+- **Metric**: Mean Absolute Error (MAE)
+- **Epochs**: 10
+- **Batch Size**: 256
+- **Embedding Dimension**: 64
+
+## Intended Use
+This model is intended to provide **movie recommendations** based on user profile clusters. By embedding user profiles and movies into a shared space, it provides recommendations by finding the best matching movies for a particular user profile.
+
+### Use Cases
+- **Personalized Movie Recommendations**: For streaming platforms, this model can serve as the core recommendation engine for suggesting movies tailored to user preferences based on demographics and past high-rated genres.
+- **User Segmentation**: The model clusters users based on demographic and genre preferences, which can also be used for analysis and targeted advertising.
+
+### Limitations
+- **Cold Start Problem**: The model may not perform optimally for new users without enough past ratings or for movies without sufficient interaction data.
+- **Demographic Constraints**: Recommendations are influenced heavily by demographic data and may not fully capture nuanced user preferences.
+- **Genre Limitation**: Genre preferences are based on past ratings, which may not always reflect the user’s evolving interests.
+
+## How to Use
+To use this model, you'll need:
+1. **Profile ID**: Identify or calculate the user’s profile ID based on demographics and genre preferences.
+2. **Movie ID**: Specify the movie IDs you want to score for a particular profile.
+
+```python
+from tensorflow import keras
+import numpy as np
+
+# Load the trained model
+model = keras.models.load_model("profile_based_recommendation_model.keras")
+
+# Example: Generate recommendations for a user with profile_id 3 for movies with IDs 10, 50, and 100
+profile_id = np.array([3])
+movie_ids = np.array([10, 50, 100])
+
+# Predict scores
+predictions = model.predict([profile_id, movie_ids])
+
+# Display predicted scores for each movie
+for movie_id, score in zip(movie_ids, predictions):
+    print(f"Movie ID: {movie_id}, Predicted Score: {score}")
+```
+
+## Dataset Citation
+If you use this model or the dataset, please cite the MovieLens dataset as follows:
+
+```
+@article{harper2015movielens,
+  title={The MovieLens datasets: History and context},
+  author={Harper, F Maxwell and Konstan, Joseph A},
+  journal={ACM Transactions on Interactive Intelligent Systems (TIIS)},
+  volume={5},
+  number={4},
+  pages={1--19},
+  year={2015},
+  publisher={ACM New York, NY, USA}
+}
+```
+
+## Acknowledgments
+Thanks to **GroupLens Research** for providing the MovieLens dataset and the open-source tools that make it accessible for research purposes.
+
+---
+
+This model card can be customized further if you want to add more specific instructions or additional use cases.