Inspiration
There are numerous events and workshops that keep happening across the campus, but many of them often go unnoticed or get lost in the flood of different activities. As a result, students miss out on opportunities that match their interests simply because they aren’t aware of them. Our project aims to solve this by building a personalized event recommendation system that surfaces relevant events based on each student’s preferences and engagement history.
What it does
It is a recommendation platform for our campus that suggests relevant events, workshops, and competitions to students based on their interests, search queries, and past attendance. The system uses natural language processing and machine learning to understand event content and user preferences, providing smart, context-aware recommendations that help students discover opportunities aligned with their goals.
Challenges we ran into
Dataset
Built With
- natural-language-processing
- python
- streamlit
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