Original title: Comparing Retrieval Methods for Academic Advisor Discovery: A Six-Method Study of 768 CS Faculty Profiles Across 9 US Universities
Biraj Subedi · September 2026
Finding the right graduate school advisor is tough. Students must match their interests with professors who study the same topics. Researchers tested six search techniques to rank professors based on an applicant's research statement. Some methods simply matched shared words. Other methods read the overall meaning of sentences, blended different scores, or reordered the final list. They tested these tools on profiles from computer science professors across nine universities. A method that reordered candidate matches worked best overall, while a simple word-matching method performed worst. Surprisingly, searching only a professor's biography worked better than searching their biography alongside their research topic tags. Adding research paper abstracts directly into the text also hurt search quality, proving that simply piling on more words can backfire.
Imagine you're trying to find a study partner by posting your interests online. If you only use your hobbies as keywords, you might miss people with similar goals. Here, the task is like matching grad students to advisors based on research statements, and the study compares different ways to search and rank profiles to find the best matches.
This helps graduate students find advisors who match their research interests more accurately. Better matching can lead to more successful graduate experiences and reduce time spent on mismatches.
The study is limited to computer science faculty at US universities and uses a small dataset of 768 profiles. It does not explain why adding paper abstracts reduces performance, only that it does. Results may not generalize to other fields or countries.
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Comparing Retrieval Methods for Academic Advisor Discovery: A Six-Method Study of 768 CS Faculty Profiles Across 9 US Universities — Biraj Subedi
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.