For BLSE, we create mono-domain embeddings using the Word2Vec toolkit 2 by training skip-gram embeddings with 300 dimensions, subsampling of 10 −4 , window of 5, negative sampling of 15 on the concatenation of the unlabeled Amazon corpora.
figure 6: The BLSE model (solid lines) compared to a variant without the target domain projection matrix M (dashed lines).