  Addressing the "vocabulary mismatch" issue in information retrieval is a
central challenge for e-commerce search engines, because product pages often
miss important keywords that customers search for. Doc2Query[1] is a popular
document-expansion technique that predicts search queries for a document and
includes the predicted queries with the document for retrieval. However, this
approach can be inefficient for e-commerce search, because the predicted query
tokens are often already present in the document. In this paper, we propose
Doc2Token, a technique that predicts relevant tokens (instead of queries) that
are missing from the document and includes these tokens in the document for
retrieval. For the task of predicting missing tokens, we introduce a new
metric, "novel ROUGE score". Doc2Token is demonstrated to be superior to
Doc2Query in terms of novel ROUGE score and diversity of predictions. Doc2Token
also exhibits efficiency gains by reducing both training and inference times.
We deployed the feature to production and observed significant revenue gain in
an online A/B test, and launched the feature to full traffic on Walmart.com.
  [1] R. Nogueira, W. Yang, J. Lin, K. Cho, Document expansion by query
prediction, arXiv preprint arXiv:1904.08375 (2019)
