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Worked with a team of 8 in Google's BGN Hackathon Autumn 2021 and created a Flutter application that matches job seekers with recruiters to tackle the issue of unemployment in African youth. The application uses a cosine similarity recommendation system to provide users with personalised job suggestions.

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prithmanickam/Africa-Reach

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Africa-Reach

Worked with a team of 8 in Google's BGN Hackathon Autumn 2021 and created a Flutter application that matches job seekers with recruiters to tackle the issue of unemployment in African youth. The application uses a cosine similarity recommendation system to provider users with personalised job suggestions.

Screenshots

Login screen Job recommendation page

How it works

  • User creates an account to login
  • If user presses the heart icon, that job posting will be saved to 'favourites', and a similar job posting will be suggesting next
  • If user presses the double-arrow icon, that job posting will not be used when recommending the next posting
  • If the user presses 'favourites', user can scroll through all the postings that they favourited which can direct them to the link to apply

Future improvement

  • Enabling filters e.g. if user just wants to see remote jobs only
  • More easy-to-use UI by swiping right or left if favourited job posting or skip
  • Improve recommendation system by using Matrix Factorization which recommends job posts based on similar user preferences
  • Enable recruiters to invite candidates by looking at CVs in a similar way
  • User inbox to check if they have recieved an invitation

Team

  • BGN Hackathon 2021 lasted between Oct 29th to Nov 1st.
  • Will be adding further iterations to develop my Flutter programming skills and as it is an application I am passionate about.

About

Worked with a team of 8 in Google's BGN Hackathon Autumn 2021 and created a Flutter application that matches job seekers with recruiters to tackle the issue of unemployment in African youth. The application uses a cosine similarity recommendation system to provide users with personalised job suggestions.

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