Bookworm

Bookworm is a personalized, mood-based book recommender using OpenAI and Google Books to suggest accessible reads and nudge people off screens. Next: accounts, location-aware access, and ratings.

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Inspiration

In an increasingly technological world, we’ve transitioned away from reading and towards spending excessive amounts of time on our phones. This has led to a significant drop in attention span - the average attention span for Gen Z individuals was 6.5 seconds. As such, we wanted to design a website that encourages people to spend more time reading by providing book recommendations.

What it does

Bookworm is a website that provides book recommendations based on an individual's mood, rather than a specific genre. We believe that by designing recommendations based on mood, individuals can access books with a more personalized, easier approach. By selecting a mood, a user will be able to see a number of book recommendations, from which they can preview the title and a short summary. Moreover, they can find ways to access these books.

How we built it

We designed a user-friendly web interface using HTML, CSS, and JavaScript. Based on user input, we used OpenAI’s API to analyze input mood parameters and find potential book matches available through Google Books' API, which is then passed back to the website to be displayed. Additionally, Google Books provides a summary for each book in its database, in addition to easy ways to access the suggested books online, making reading more accessible for users.

Challenges we ran into

  • Effectively using and connecting APIs from OpenAI and Google Books to our website to display recommendations.
  • Ensuring that our hack is effective in using mood-based parameters and providing suitable recommendations.

Accomplishments that we're proud of

As it was most of our team members' first hackathon, we're proud to have worked together to design a web application that brought many different parts together to ultimately promote reading among individuals.

What we learned

  • How to effectively use sentiment/mood-based parameters to provide book suggestions and ways to access each suggested book.
  • Designing a user-friendly interface to complement the backend design of our website.

What's next for Bookworm

We believe Bookworm has a lot of potential to be improved and used commercially. Below are a few next steps we can take to further improve Bookworm.

  • We could add personalized accounts to track user recommendation history.
  • We can add a location tracking system to provide for easier book accessibility in local libraries.
  • We can add a rating system and an option for user reviews to analyze our recommendations and their effectiveness.

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