A Decision Support System for Conference Session Selection using Natural Language Processing and Machine Learning

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Conference attendees are often faced with selecting from hundreds 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 aim to develop a tool leveraging machine learning (ML) and natural language processing (NLP) techniques such as topic modeling (BERTopic) and semantic matching. By creating and matching embeddings of conference presentation abstracts and titles, the tool will provide improved query matching compared to keyword searching. Attendees 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 an abstract of a paper most relevant to their area of interest and receive a collection of relevant conference presentations. We hope that this tool will enable academics and practitioners to rapidly identify relevant presentations, fostering knowledge exchange and professional network exploration.