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

arXiv: https://arxiv.org/abs/2308.02055 · HTML: https://mlsanity.com/paper/2308.02055/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep Pairwise Learning To Rank For Search Autocomplete](https://mlsanity.com/paper/2108.04976/) (2021)

## Built on by (most cited first, 1 of 1)

- [Synthetic Prefixes to Mitigate Bias in Real-Time Neural Query Autocomplete](https://mlsanity.com/paper/2510.01574/) (2025)

## Abstract (arXiv)

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.