/LightFM_Recommender_System

Primary LanguageJupyter Notebook

Introduction

This project focuses on building and evaluating a movie recommendation system using a dataset of user-movie interactions.

Project Steps

Step 1: Data Loading and Preprocessing

  • Read movies.txt and create a DataFrame movies_df.
  • Read movie_users_10_20.txt and create a DataFrame movie_users_df.
  • Convert relevant columns to appropriate data types.

Step 2: Data Merging

  • Merge movie_users_df and movies_df on movie_id.

Step 3: Creating User-Item Interaction Matrix

  • Pivot the merged DataFrame to create an interaction matrix with users as rows, movies as columns, and ratings as values.

Step 4: Creating Sparse Matrix

  • Map user and movie IDs to unique indices.
  • Create a COO (Coordinate Format) sparse matrix from the interaction data.
  • Optionally convert the COO matrix to CSR (Compressed Sparse Row) format.

Step 5: Model Training and Evaluation

  • Initialize and train the LightFM model with WARP loss.
  • Evaluate the model using precision and recall at k.

Step 6: Generating Recommendations

  • Define a function get_top_recommendations to predict top N movie recommendations for a given user.
  • Generate and display recommendations for selected users.

Future Work

For future enhancements, we could consider:

  • Integrating more diverse data sources for richer user and item profiles.
  • Experimenting with different model parameters and advanced algorithms for improved accuracy.
  • Implementing a more interactive and user-friendly interface for users to interact with the recommendation system.

This project is a stepping stone towards building more sophisticated and user-centric recommendation systems in the rapidly evolving field of data science and machine learning.