Deep Movie Recommender System ( AutoEncoder)

[ayush-09]

Introduction

This project aims to develop a deep movie recommender system using AutoEncoders. The recommender system will leverage advanced deep learning techniques to analyze user preferences and provide highly accurate movie recommendations. By utilizing AutoEncoders, the system will learn intricate patterns in the movie ratings data and generate personalized recommendations based on users' unique tastes and interests.

Methodology

• Data Acquisition: Obtain the MovieLens dataset containing movie ratings provided by users.
• Data Preprocessing: Preprocess the ratings data by handling missing values, normalizing ratings, and transforming the data into a suitable format.
• Model Architecture Design: Design a deep AutoEncoder model using PyTorch with appropriate layers and neuron configurations.
• Model Training: Train the AutoEncoder model using the preprocessed ratings dataset and optimize its parameters using the Adam optimizer and Mean Squared Error loss.
• Model Evaluation: Evaluate the trained model using a separate validation dataset to measure its performance in reconstructing movie ratings.
• Recommendation Generation: Generate personalized movie recommendations by reconstructing ratings and recommending higher-rated movies.
• Model Optimization: Apply techniques like regularization, hyperparameter tuning, and ensemble methods to optimize the model's performance.

[ayush-09]

@khusheekapoor Please assign this task to me

[khusheekapoor]

@ayush-09 - you can go ahead! We are assigning you 21 days for this project, after which it will be assigned to someone else if not completed. All the best! Name the file as: algorithm_dataset.ipynb and link it in the readme of the labeled directory as algorithm - dataset.