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Development of a Machine Learning-Powered Book Discovery Platform with Social Features
  1. case
  2. Development of a Machine Learning-Powered Book Discovery Platform with Social Features

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Development of a Machine Learning-Powered Book Discovery Platform with Social Features

buildableworks.com
Information technology
Media
eCommerce

Challenges in Book Discovery and Recommendation Systems

Current book recommendation systems prioritize popularity and paid placements over literary merit, while social-based algorithms create echo chambers of recycled content. Readers struggle to discover high-quality books outside mainstream channels, and traditional bookstore curation favors publishers with marketing budgets.

About the Client

A startup focused on creating innovative digital solutions for book readers and publishers

Key Project Goals

  • Create a merit-based book discovery system using machine learning
  • Develop personalized recommendation capabilities through user behavior analysis
  • Implement digital shelf management for reading tracking
  • Foster literary community engagement through social features
  • Deliver an intuitive interface for book search via text/image recognition

Core System Capabilities

  • Machine learning algorithm for book recommendations
  • Text/image scan search functionality
  • Personalized reading shelf management system
  • Social sharing of shelves and reviews
  • User profile management with reading history

Technology Stack Requirements

.NET Core
Entity Framework
Xamarin.Forms
Angular
Google Vision API

External System Integrations

  • Google Books API
  • Ingram Content Group API
  • Barcode scanning SDK
  • Cloud storage for book metadata

System Quality Attributes

  • Scalability to handle 3.3M+ book database
  • Real-time recommendation processing
  • High-accuracy image recognition
  • Cross-platform mobile compatibility
  • Data privacy compliance (GDPR)

Expected Business Outcomes

The platform will disrupt traditional book discovery channels by providing merit-based recommendations, creating new revenue streams through publisher partnerships, and building a engaged community of readers. The machine learning system will reduce discovery time by 60% while increasing exposure for underrepresented titles, with potential for subscription-based premium features and affiliate book sales.

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