“It’s Who You Know”: Feasibility of a Hybrid Recommender System to Connect Students with Informal Social Networks of Pitt Researchers

Abstract

Working in a large organization, it takes time, persistence, and luck to find someone with the right skills and interests for a project, or simply for great collaborations. Students in a large research university such as Pitt experience a similar barrier to finding ideas, topics, and projects that could tap into their personal interests and energize their learning. Faculty experience this barrier when they can't draw students to join their research or programs. It's not that it's impossible. We have seen students make the cold calls or show up to faculty offices, ask about projects, and take risks in trying courses in unfamiliar disciplines. But we believe it is possible to help students explore the very large number of learning opportunities here. We will test the concept of an online system that can curate and filter vast amounts of information about the research, career, and learning going on at Pitt. The system would aggregate and interconnect this vast data by learning from the selections that students make, filtering through and exploring options, much like how Netflix learns what movies to recommend. Students' choices could result in personalized education, career pathways, and research collaborations for themselves, faculty, and future students.

Collaborators

Eliza Beth Littleton
Dmitriy Babichenko
Ravi Patel
Lorin Grieve
Peter Brusilovsky
Kar-Hai Chu
Chun-Hua Tsai
Jordan Ariel Barria Pineda
Shreya Shenoy
Hung Chau
Zara Blum