Inspiration
Currently, finding student organizations at UF involves you finding the organizations. But what if the organizations could find you specifically? SwampNet aims to allow this approach by matching students with organizations based on several aspects of their personality.
What it does
SwampNet first asks the user to input their major, interests, and an overall biography. It then employs a powerful algorithm that thoroughly analyzes the user's input, and outputs a sequence of 5-10 clubs in a specific sequence determined by how close the organization's description matches that of the student's. The student can then swipe right to "favorite" that organization (adding it to a list of "favorites") or swipe left to ignore that suggestion. Once they are finished with this, the student can then view more information about the organizations they have favorited.
How we built it
In order to build SwampNet we mainly relied on the React Native and Expo frameworks. In addition, we used Firebase to store a small amount of data. React Native allowed us to design all our screens in a simple way while also allowing us to implement the behind-the-scenes processes that occur when someone picks up our app.
Challenges we ran into
We ran into many challenges while developing SwampNet. Not all of the members of the team were able to work on the codebase at the same time, and at various points throughout the programming process we were forced to restart some of our files from scratch due to issues with GitHub. Moreover, none of the team members were very skilled in frontend programming, so there was a bit of struggle in creating that part of the app.
Accomplishments that we're proud of
One of the best accomplishments while creating this app was the algorithm we used. As explained above, it works based on the user's definitions and can be easily modified to include data from the user's swipes as well.
What we learned
From working on SwampNet, we learned more about the React Native framework and also came up with creative ways to interpret data.
What's next for SwampNet
There are several ways in which SwampNet can be improved that we did not have the time to implement or were not able to figure out how to implement them. Regarding the algorithm/AI, we could train the computer to recognize details even more precisely and infer aspects of a student's personality based on the information provided.
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