Generate Paper Recommendations From a Seed List in Semantic Scholar Into a Google Sheet

The Scenario

A postdoctoral researcher is running a meta-analysis and has 12 seed papers already confirmed for inclusion. The next step in the protocol is candidate expansion: find papers similar to the seeds that the team hasn't encountered yet. The standard move is to hand the list to a research assistant, ask them to mine Semantic Scholar for recommendations, and wait two days. There is no research assistant this quarter.

The bad version:

Open the Semantic Scholar page for seed paper 1, scroll to the Recommended Papers section, copy the first 10 titles, switch to the sheet, paste — then reformat the paste because the clipboard included links and author names in a single column.
Repeat for seed paper 2. Notice the recommendations for paper 2 overlap heavily with paper 1, but you have no way to deduplicate until you've done all 12 and manually compared.
After 90 minutes you have a messy, partially overlapping candidate list with inconsistent formatting across 12 pastes.

The expansion step is supposed to surface candidates the team missed — not occupy a full morning of mechanical copying.

The Easy Way: One Prompt in SheetXAI

SheetXAI is an AI agent that lives inside your Google Sheet. It reads the seed paper IDs in column A, calls Semantic Scholar's recommendation engine for each one, and writes the suggested papers as rows in a new sheet — ready for triage.

Here is the prompt for this task:

For each paper ID in column A, call Semantic Scholar's recommendation engine and return the top 10 recommended papers with their title, year, and authors as new rows on a sheet called Recommendations

What You Get

A Recommendations sheet with one row per suggested paper.
Columns: Seed Paper ID (from column A), Title, Year, Authors.
All 120 candidates (12 seeds x 10 recommendations) land in a flat table — ready for deduplication and triage in one sort-and-filter operation.
Duplicates across seeds appear as multiple rows, so you can see which papers the recommendation engine independently surfaced from different starting points.

What If the Data Is Not Quite Ready

Column A has titles, not Semantic Scholar paper IDs

For each paper title in column A, resolve it to a Semantic Scholar paper ID, then call the recommendation engine and write the top 10 recommended papers with title, year, and authors into the Recommendations sheet with a Seed Title column

You want to exclude papers already in the seed list from the recommendations

For each paper ID in column A, get the top 10 Semantic Scholar recommendations, filter out any recommended paper whose ID appears in column A, and write the remaining results to the Recommendations sheet with title, year, authors, and seed paper ID

You need recommendations filtered to a specific field of study

For each paper ID in column A, call Semantic Scholar's recommendation engine, filter results to papers tagged under the Biology field of study, and write the top 5 matching recommendations per seed to the Recommendations sheet with title, year, and authors

Resolve seed IDs, get recommendations, deduplicate, and rank by citation count in one pass

For each paper ID in column A, fetch the top 10 Semantic Scholar recommendations, exclude papers already in column A, write each unique recommendation to the Recommendations sheet with title, year, authors, and citation count, and sort the full table by citation count descending so the most-established candidates appear first

Try It

Get the 7-day free trial of SheetXAI and open any sheet with a column of confirmed seed paper IDs. Ask SheetXAI to expand the reading list into a structured candidate table — and start triage the same afternoon instead of waiting on a research assistant.