GTA (Go To App): A KNN and Cosine Similarity Framework for Campus-Exclusive Skill-Based Peer Discovery and Real-Time Collaboration
Year: 2026
Type: article
Abstract: University campuses contain diverse student skills, yet finding compatible teammates for projects, hackathons, and peer learning often depends on informal social networks. This paper presents GTA (Go To App), a campus-exclusive platform for skill-based peer discovery and real-time collaboration. Student proficiency is represented by weighted skill vectors, with weights of 0.3, 0.6, and 1.0 for beginner, intermediate, and advanced levels. A K-Nearest Neighbours engine uses cosine similarity to rank profiles and return the top 20 matches through a PostgreSQL Remote Procedure Call. Mandatory skill onboarding provides a non-empty profile for each new user. The platform combines institutional email authentication, mutual-acceptance messaging through Socket.io, campus communities, and events. A four-layer verification pipeline incorporates self-rating, timed quizzes, post-collaboration peer endorsement, and GitHub repository linking. The prototype uses React, Supabase, and AWS services. Evaluation involved 50 test student profiles and a database of 500 seeded profiles. All 50 profiles received a non-empty match list on their first request. Across 100 requests, mean search latency was 87 ms with a standard deviation of 23 ms. Deployment at 15,000-student scale and an annual infrastructure cost of approximately INR 20,750 remain projections. The framework integrates profile-based recommendations and communication within an authenticated campus environment.
Author: Vivek S D
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7224924868