https://github.com/shrii0807/novel_recommendation_system

This application is a novel recommendation system built using Streamlit, MongoDB, and Faker for data generation. The system allows users to browse novels, explore genres, and receive personalized recommendations based on selected genres and rating preferences.
https://github.com/shrii0807/novel_recommendation_system

mongodb novels python recommendation-system streamlit

Last synced: 30 days ago
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This application is a novel recommendation system built using Streamlit, MongoDB, and Faker for data generation. The system allows users to browse novels, explore genres, and receive personalized recommendations based on selected genres and rating preferences.

• Host: GitHub
• URL: https://github.com/shrii0807/novel_recommendation_system
• Owner: Shrii0807
• Created: 2024-11-12T11:10:02.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-11-22T11:44:29.000Z (about 1 month ago)
• Last Synced: 2024-11-22T12:29:12.799Z (about 1 month ago)
• Topics: mongodb, novels, python, recommendation-system, streamlit
• Language: Python
• Homepage:
• Size: 18.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Novel_Recommendation_System

This application is a novel recommendation system built using Streamlit, MongoDB, and Faker for data generation. The system allows users to browse novels, explore genres, and receive personalized recommendations based on selected genres and rating preferences.

![image](https://github.com/user-attachments/assets/01c9dcd0-f0c4-4c41-9814-27066598c950)

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# Features

Login Page:

![image](https://github.com/user-attachments/assets/5605c290-881d-468b-9e96-2a49671bcf50)

![image](https://github.com/user-attachments/assets/be5fe562-377d-44c3-afe2-5cb76e3517a6)

Browse Novels: Users can view novels with details like title, author, genres, pages, and ratings.

Genre-Based Recommendations: Users can select one or more genres to receive recommendations tailored to their preferences.

Rating-Based Sorting: Recommendations can be prioritized based on a user-defined minimum rating threshold.

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# Project Structure

novels.json: Dataset containing novel information such as title, author, pages, genres, and ratings.

Vapp.py: Main application script for the Streamlit interface and MongoDB interaction.

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# Applications

E-commerce: Personalized product recommendations, dynamic pricing, visual search.

Entertainment: Content personalization for movies, music, and games.

Social Media: Feed curation, friend suggestions, targeted ads.

Healthcare: Personalized treatment plans, drug discovery, health management.

Education: Customized learning paths, skill recommendations, tutoring systems.

Travel: Tailored itineraries, real-time adjustments, local experience suggestions.

Finance: Investment strategies, credit scoring, wealth management.

HR & Recruiting: Job matching, career development, automated screening.

Food & Dining: Restaurant recommendations, meal kits, personalized recipes.

News: Personalized news feeds, topic-based content.

Real Estate: Property and neighborhood recommendations.

Sports & Fitness: Workout plans, fitness tracking, sports event recommendations.