Seasonality Based Reranking of E-commerce Autocomplete Using Natural Language Queries
This addresses the need for more relevant seasonal suggestions in e-commerce search, but it is incremental as it builds on existing autocomplete systems.
The paper tackled the problem of improving query autocomplete relevance in e-commerce by incorporating seasonality signals, resulting in enhanced autocomplete relevance and business metrics as evaluated end-to-end.
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.