https://github.com/mahikshith/rec_sys_with_auto_encoder

https://github.com/mahikshith/rec_sys_with_auto_encoder

Last synced: 11 months ago
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• Host: GitHub
• URL: https://github.com/mahikshith/rec_sys_with_auto_encoder
• Owner: mahikshith
• Created: 2024-08-04T16:15:49.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-04T16:24:16.000Z (almost 2 years ago)
• Last Synced: 2025-01-14T13:16:32.563Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 856 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
Introduction : 

This project implements a movie recommender system using Autoencoders. The core idea is to learn a compressed representation (latent space) of user-movie interactions, which can be used to predict user preferences for unseen movies.

Autoencoder Architecture

An Autoencoder is a neural network architecture composed of two main components:

Encoder: Maps input data (user-movie ratings matrix) to a lower-dimensional latent space representation.

Decoder: Reconstructs the original input data from the latent space representation.

Feature Extraction:

The encoder part of the Autoencoder is crucial for extracting features from the user-movie rating matrix.

By training the Autoencoder to reconstruct the original ratings matrix, it learns to capture underlying patterns and relationships between users and movies.

The latent space representation generated by the encoder acts as a feature map, containing compressed information about users and movies.

Key Components

User-Item Matrix: A sparse matrix representing user ratings for different movies.

Autoencoder Model: A neural network architecture with an encoder and decoder.

Latent Space: The compressed representation of user-movie interactions generated by the encoder.

Recommendation Generation: Using the learned latent space, similar users or movies can be identified, and recommendations can be made based on their preferences.

Steps Involved

Autoencoder Training: Train the Autoencoder to reconstruct the user-movie rating matrix.

Feature Extraction: Extract the latent space representations from the trained encoder.

Similarity Calculation: Calculate similarity between users or movies based on their latent representations.

Recommendation Generation: Recommend movies to users based on the similarity scores.

Autoencoders learn efficient data representations.

Latent space captures underlying patterns in user-movie interactions.

Recommender systems predict user preferences based on latent features.

Feature extraction is performed by the encoder part of the Autoencoder.

Similarity between users or movies is calculated in the latent space.

Evaluation metrics assess the performance of the recommender system.

Incorporating additional features can enhance recommendation accuracy.

Experiment with different Autoencoder architectures for improved results.

 Combine Autoencoders with other recommendation techniques for hybrid approaches.