Movie Recommender System using TensorFlow is a collaborative filtering-based recommendation engine designed to provide personalized movie suggestions based on user preferences. This project showcases proficiency in machine learning and deep learning concepts, with a focus on TensorFlow as the primary framework. Key components and achievements include:
Collaborative Filtering Model:
Implemented a collaborative filtering approach using user and movie embeddings to capture user preferences and movie characteristics. TensorFlow Framework:
Leveraged TensorFlow, a leading machine learning library, to design, train, and deploy the recommendation model. Embedding Layers:
Designed and incorporated embedding layers for users and movies to learn latent representations, enabling the model to capture intricate patterns in user-movie interactions. Model Training:
Trained the model using a sample dataset, optimizing it with stochastic gradient descent to minimize mean squared error loss. Hyperparameter Tuning:
Explored and adjusted hyperparameters, such as embedding size, to optimize the model's performance. Real-time Predictions:
Implemented the model to make real-time predictions for user-movie interactions, showcasing its effectiveness in generating accurate recommendations. Scalability:
Ensured scalability by using TensorFlow Serving for deploying the recommendation model in production environments. Python Programming:
Developed the recommendation system using Python, incorporating libraries such as NumPy and Pandas for data processing. Documentation:
Created comprehensive documentation covering model architecture, data preprocessing steps, and deployment processes for seamless understanding and future maintenance. Continuous Learning:
Demonstrated commitment to continuous learning by staying informed about advancements in machine learning and recommendation systems.