Convert read_time to confidence weight: ( c_ui = 1 + \alpha \cdot \log(1 + t_ui) ).
In advanced systems, you would the RoBERTa embeddings with the WALS objective – this is the core idea behind recommendation transformers like BERT4Rec or Amazon’s SMILES, but at higher computational cost. wals roberta sets top
on platforms like Instagram or Patreon, though it lacks a broad commercial footprint. Convert read_time to confidence weight: ( c_ui =
If your RoBERTa outputs 768-dim and your WALS rank is 200, you need a projection layer. Failing to set this correctly causes dimension mismatch errors. wals roberta sets top