01
Build comparable text
Your weighted expertise items and each paper’s title and abstract are normalized and
split into overlapping chunks when they exceed the model’s practical context size.
Imported conference topics remain available only as display/filter metadata.
02
Embed locally
The app uses the quantized all-MiniLM-L6-v2 sentence-transformer model.
It produces 384-dimensional vectors in a Web Worker, using WebGPU when available and a
local WebAssembly fallback otherwise. Model files are served with the app.
03
Measure topical proximity
Chunk vectors are mean-pooled and normalized. Cosine similarity compares each paper with
every profile item. The final score blends the weighted profile centroid (70%) with the
strongest individual expertise match (30%).
04
Keep judgment human
The strongest matching profile items explain each base score. Interested decisions add
a small similarity boost, while not-interested papers and manual negative phrases apply
stronger penalties. Similarity never becomes a bid automatically, and conflicts remain
part of the official conference process.