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

Adithya Rajan; Prateek Verma; Shan Zhong; Xiaoyu Liu

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

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arxiv 2308.02055 v1 pith:VLCJQZQP submitted 2023-08-03 cs.IR cs.CLcs.LG

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

Prateek Verma , Shan Zhong , Xiaoyu Liu , Adithya Rajan This is my paper

classification cs.IR cs.CLcs.LG

keywords autocompletequeriesseasonalitye-commercelanguagemodelnaturalranking

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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment
cs.IR 2026-02 conditional novelty 6.0

A RAG-and-DPO trained language model that generates whole query-completion lists improved production QAC in a live test (5.44% fewer keystrokes, 3.46% more adoptions), but the offline metrics reuse the training verifiers.