OSU Alumni Association events bring together students, alumni, employers, and other members of the Oregon State community, but attendees often face a fundamental networking challenge: How do I know who I should meet?
This project proposes developing a web or mobile application that uses participants' professional information, interests, skills, career goals, and event-specific networking objectives to recommend the people they are most likely to benefit from meeting at an OSU Alumni Association event.
Rather than functioning as a traditional attendee directory, the application would provide personalized, explainable connection recommendations. For example, a student interested in healthcare careers might be matched with an alum who works in healthcare recruiting, while an alum seeking to mentor students in a particular field could be matched with students who have expressed interest in that career area.
The project would explore the technical challenges of profile analysis, recommendation/matching algorithms, user experience, data privacy, authentication, and potentially AI/ML techniques. The ultimate goal is to create a prototype that demonstrates how technology can make OSU networking events more intentional, productive, and accessible for students and alumni.
Students and alumni frequently attend professional networking events to make meaningful connections, but large events can make it difficult to identify the people most relevant to their individual goals.
Traditional event-registration systems can tell attendees who is attending, but generally do not answer:
OSU Alumni Association currently supports career networking, mentoring, alumni engagement, and student/alumni programming through initiatives such as OSU Connections. This project would investigate whether a dedicated recommendation experience could make in-person and virtual networking significantly more personalized and effective.
The project team will:
Develop a functional prototype that allows students and alumni to create or import a professional profile and identify their goals for attending an event.
Develop a recommendation/matching system that identifies potentially valuable connections based on factors such as:
Provide personalized recommendations that prioritize the people each attendee should consider meeting.
Make recommendations explainable by providing users with a concise explanation of why a particular connection may be relevant.
Design an intuitive event experience that allows attendees to review, prioritize, save, and potentially initiate connections with recommended individuals.
Explore AI/ML or other recommendation approaches for extracting relevant information from professional profiles and determining compatibility between attendees.
Incorporate privacy and consent considerations into the application architecture and user experience.
Develop a scalable technical architecture that could potentially support different OSU Alumni Association events, student populations, alumni communities, and professional networks.
Evaluate the effectiveness of the prototype through usability testing and/or simulated networking scenarios.
Karla Rockhold
