Contextual BERT: Conditioning the Language Model Using a Global State
This work addresses personalization in recommendation systems, particularly for fashion, but is incremental as it builds on BERT with added context conditioning.
The authors tackled the problem of personalizing language model predictions by conditioning BERT on a global context, such as customer information, and applied it to completing fashion outfits. Their methods significantly improved personalization compared to existing approaches.
BERT is a popular language model whose main pre-training task is to fill in the blank, i.e., predicting a word that was masked out of a sentence, based on the remaining words. In some applications, however, having an additional context can help the model make the right prediction, e.g., by taking the domain or the time of writing into account. This motivates us to advance the BERT architecture by adding a global state for conditioning on a fixed-sized context. We present our two novel approaches and apply them to an industry use-case, where we complete fashion outfits with missing articles, conditioned on a specific customer. An experimental comparison to other methods from the literature shows that our methods improve personalization significantly.