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Seasonal Relevance in E-Commerce Search

Authors:

Roberto Fernández Galán,

Dongmei JiaAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 4293 - 4301

https://doi.org/10.1145/3459637.3481951

Published: 30 October 2021 Publication History

Abstract

Seasonality is an important dimension for relevance in e-commerce search. For example, a query jacket has a different set of relevant documents in winter than summer. For an optimal user experience, the e-commerce search engines should incorporate seasonality in product search. In this paper, we formally introduce the concept of seasonal relevance, define it and quantify using data from a major e-commerce store. In our analyses, we find 39% queries are highly seasonally relevant to the time of search and would benefit from handling seasonality in ranking. We propose LogSR and VelSR features to capture product seasonality using state-of-the-art neural models based on self-attention. Comprehensive offline and online experiments over large datasets show the efficacy of our methods to model seasonal relevance. The online A/B test on 784 MM queries shows the treatment with seasonal relevance features results in 2.20% higher purchases and better customer experience overall.

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Cited By

Sousa HWard AAlonso ONejdl WAuer SKarras OCha MMoens MNajork M(2025)Don't Forget This: Augmenting Results with Event-Aware SearchProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3704119(1056-1059)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3704119
Tian AZhou AWang YJian XChen L(2024)Efficient Index for Temporal Core Queries over Bipartite GraphsProceedings of the VLDB Endowment10.14778/3681954.368196517:11(2813-2825)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681965
Ward AAlonso OSerra ESpezzano F(2024)Empowering Shoppers with Event-focused SearchProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679235(5294-5298)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679235

Index Terms

Seasonal Relevance in E-Commerce Search
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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Published: 30 October 2021

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November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Cited By

Sousa HWard AAlonso ONejdl WAuer SKarras OCha MMoens MNajork M(2025)Don't Forget This: Augmenting Results with Event-Aware SearchProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3704119(1056-1059)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3704119
Tian AZhou AWang YJian XChen L(2024)Efficient Index for Temporal Core Queries over Bipartite GraphsProceedings of the VLDB Endowment10.14778/3681954.368196517:11(2813-2825)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681965
Ward AAlonso OSerra ESpezzano F(2024)Empowering Shoppers with Event-focused SearchProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679235(5294-5298)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679235

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