Seasonality Based Reranking of E-commerce Autocomplete Using Natural Language Queries

TL;DR

This paper introduces a neural network-based NLP method to incorporate seasonality into e-commerce query autocomplete ranking, aiming to enhance relevance and business outcomes.

Contribution

It presents a novel approach to integrate seasonality signals into QAC ranking models using neural networks, with comprehensive evaluation.

Findings

Seasonality improves autocomplete relevance.

Incorporating seasonality boosts business metrics.

Neural network models effectively capture seasonal patterns.

Abstract

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.