Jokes Rec

A collaborative filtering and content-based recommendation system for jokes.

Github Link: Jokes Rec

Project Overview

The Jokes Recommendation System is designed to provide personalized joke suggestions to users based on their individual preferences. By analyzing user ratings and employing advanced recommendation algorithms, the system aims to enhance user engagement and satisfaction.

Technologies Used

The project is developed using Python, utilizing libraries such as Pandas for data manipulation, NumPy for numerical computations, and Scikit-learn for implementing machine learning algorithms.

Features and Functionalities

  • Data Preprocessing: The system processes and normalizes user ratings to ensure consistency and accuracy in the recommendation process.

  • Collaborative Filtering: Employs collaborative filtering techniques to identify patterns in user preferences and suggest jokes that similar users have enjoyed.

  • Content-Based Filtering: Utilizes content-based filtering methods to recommend jokes based on their features and the user’s past preferences.

  • Hybrid Recommendation System: Combines both collaborative and content-based filtering approaches to provide more accurate and diverse joke recommendations.

These features collectively enhance the user experience by delivering personalized and engaging joke suggestions.

For a more detailed exploration of the project, you can visit the Jokes Recommendation System GitHub Repository.