Seasonality Based Reranking of E-commerce Autocomplete Using Natural
  Language Queries
Prateek Verma Shan Zhong Xiaoyu Liu Adithya Rajan
Query autocomplete (QAC) also known as typeahead, suggests list of complete
queries as user types prefix in the search box. It is one of the key features
of modern search engines specially in e-commerce. One of the goals of typeahead
is to suggest relevant queries to users which are seasonally important. In this
paper we propose a neural network based natural language processing (NLP)
algorithm to incorporate seasonality as a signal and present end to end
evaluation of the QAC ranking model. Incorporating seasonality into
autocomplete ranking model can improve autocomplete relevance and business
metric.