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Team Name: We Pi

Project Title: News Article Recommender System

Inspiration💡

Google News Recommendation System working is what inspired us to do this project.

What it does🏗️

• Recommends the Top-10 headlines based on the user's interest or choice of reading
• We give the most relevant ones making them suitable for users to select. ## How we built it
• Python
• Jupyter Notebook
• Machine Learning
• Streamlit

Challenges we ran into⛔

• We tried to work on abstract summarization but due to a few errors in LSTM Model, we were unable to proceed.
• Also there were difficulties leveraging the IBM Linux One Platform. But consistent support from mentors helped us overcome these difficulties.

Accomplishments that we're proud of🏅

• We were able to get the most relevant search results for any user-entered article headline.
• We are able to recommend the headlines based on the articles.
• Create a front-end so that it is not only notebook accessible but could be used by any normal users.
• Display the articles with their short summaries which eases the choosing time for the user.

What we learned👨🏼‍🏭

• Different Python Modules are used to do NLP-based processing
• Learned to implement our modules in IBM Cloud-based platform
• Different steps involved in NLP-based projects

What's next for We Pi Team⏭️

• Build a SaaS product and make it open-source for everyone to access and stay updated.
• Working to integrate Web Scraping to support different other news categories.

Built With

• ibm-cloud
• jupyter-notebook
• python