Overview

This mobile app serves book readers by delivering book Discoveries via a machine learning algorithm, leveraging a database of 3.3M+ books. Book readers can perform a text or image scan search for a book they have enjoyed and receive excellent book discoveries based on that book. This app also features Shelves, a means of managing the titles that readers have read or want to read. Finally, this app creates space for social interaction in the literary community through shared Shelves, ratings & reviews, and more!

Opportunities

Finding the next great book to read can be a challenge. When placements in bookstores and bestseller lists are sold to the highest bidder, books are selected by contest of popularity rather than merit. Popular book recommendation engines rely on social interactions to deliver recycled content to users, further exacerbating the vicious cycle of the bestseller list. These problems created the opportunity for a new solution to finding the next great book.

Design

Methods: business modeling, user research, wireframes, high-fidelity prototypes

The design opportunity for this discovery engine presented itself due to monetization strategies in the literary industry serving publishers far better than book readers. We built this mobile app to cater to the needs of book readers by collecting and analyzing user research and marketing data to inform design. Following an iterative User-Centered Design process ensured that we would reach our goal of helping book readers discover new titles to love while delivering an elegant user interface.

Book Recommendation App Screenshot
Book Recommendation App Screenshot
Book Recommendation App Screenshot
Book Recommendation App Screenshot

Design Challenge & Process

This project came to us as a startup concept. Our client had a basic idea for the functionality of the system and we refined that idea into a clear product vision. We were tasked with ideating, researching, prototyping, and building this app to serve book readers in their search for the next great book. Through several iterations of prototyping and continuous user research and testing, we ensured that our MVP would be as close as possible to the product vision without having real user data.

Architecture and Tech Stack

Methods:

  • Backend: .NET Core, Entity Framework, LINQ,, MSSQL Server, Linux, Machine Learning
  • Admin Panel: Angular, Typescript, Bootstrap, ReactiveX, HTML, CSS/SCSS
  • Mobile Application: C#, XAML, Xamarin.Forms, ReactiveX

Integrations: Google Books, Google Vision, Ingram

This app leverages machine learning and advanced algorithms to facilitate book discovery for readers. The experience has been adopted to reflect and improve upon the experience of shopping in a book store. A priority of the service uses data collection to provide what is often missing in the book distribution market. Saving books to personal shelves, keeping track of books read/reading, and easily searching for books through cover scanning or barcode readers are the primary features of this application.

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