Date of Award

6-2025

Degree Name

MS in Industrial Engineering

Department/Program

Industrial and Manufacturing Engineering

College

College of Engineering

Advisor

Puneet Agarwal

Advisor Department

Industrial and Manufacturing Engineering

Advisor College

College of Engineering

Abstract

Conference attendees are faced with selecting from hundreds to thousands of presentations and sessions in pursuit of new findings and methods relevant to their area of interest, an overwhelming amount of information from which to clearly make a decision. To address this, we developed a decision support system leveraging natural language processing (NLP) techniques such as semantic matching. By creating and matching embeddings of conference presentation abstracts and titles, the application provides improved query matching compared to keyword searching. We introduce Session Scout, a novel conference decision support system built upon a semantic retrieval framework. Session Scout is designed to move beyond keyword-matching by understanding the meaning and context behind an attendee's interests and the content of conference presentations. Users can select from a variety of input types, including keyword selection and natural language query, and receive a collection of presentations with abstracts semantically matching the input. Notably, users can input longer queries (e.g. an abstract of a paper most relevant to their area of interest) and receive a collection of relevant conference presentations. Key contributions of this work include the application of semantic matching to the conference presentation recommendation problem. They also include the evaluation of Session Scout's model on two unsupervised datasets, including the use of LLM as a judge to address this challenge. Additionally, Session Scout has been deployed and tested at a live conference, receiving positive user feedback.

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