Reading through the NewsQ challenge, we found the real-world applications of the project really interesting. Because our team had members with interests that branched out from computer science, the idea of creating back-end code
What it does
It takes into consideration a variety of factors which are assigned weights based off their relative importance. The weighted values for each factor are used to calculate a final rating for an article. The articles are then arranged according to the ratings.
How I built it
We used Python as the framework for working with different API's to pull data
Challenges I ran into
Collecting website data, using effective APIs for our algorithms, debugging for various errors when utilizing API and dealing with data
Accomplishments that I'm proud of
When we were almost done with our project, an API stopped working and we felt doomed for a short time. When we were able to work through these issues, there was an amazing sense of accomplishment that came over the team, and it lifted our spirits. Accomplishments like this are a large aspect of what makes hackatons a good time!
What I learned
We learned data scraping the web with multiple APIs, developing algorithms to quantify that scraped data, and outputting that data in a spreadsheet
What's next for Optimizing News Recommendation Algorithms
Finetune the algorithm and add more factors to make the article ranking more accurate (ex: check amount of ad content, utilizing a UK fact checker, etc.)
Utilize machine learning by creating a training set and applying attributes known to make an article more or less reliable
Create a front end interactive user-friendly interface to allow users to browse unbiased and trustworthy articles of their interest