A web-based scientific articles recommendations system that recommends scientific articles of user interest based on text classification Doc2Vec modeling scheme and cosine similarity measure.
A Signup module from which users will be required to register themself in the application. The user will get registered once the admin will approve it.
Only registered users can log into the application.
Admin can manage users means it can approve user, remove users and view user data through admin dashboard.
Admin can add scientific articles data to the database having abstract and area through admin dashboard. Once a new article is added the model training will be triggered autmatically.
A webpage which takes article abstract which is not added yet in the system as input by the user and on clicking generate recommendations the application infers user article vector. Then it shows articles recommendations by computing cosine similarity between user vector and already added article vectors in descending order.
- Django
- SQLite3
- Bootstrap
- Gensim
- NLTK
- Numpy & SciPy
- Pandas
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent