research-article

Treating Cold Start in Product Search by Priors

Authors:

Vamsi SalakaAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 77 - 78

https://doi.org/10.1145/3366424.3382705

Published: 20 April 2020 Publication History

Get Access

Abstract

New products in e-commerce platforms suffer from cold start, both in recommendation and search. In this study, we present experiments to deal with cold start in search by predicting priors for behavioral features in learning to rank set up. The offline results show that our technique generates priors for behavioral features which closely track posterior values. The online A/B test on 140MM queries shows that treatment with priors improves new products impressions and increased customers engagement pointing to their relevance and quality.

References

[1]

Ricardo Campos, Gaël Dias, Alípio M. Jorge, and Adam Jatowt. 2014. Survey of Temporal Information Retrieval and Related Applications. ACM Comput. Surv. 47, 2, Article 15 (Aug. 2014), 41 pages. https://doi.org/10.1145/2619088

Crossref

Google Scholar

[2]

Anlei Dong, Yi Chang, Zhaohui Zheng, Gilad Mishne, Jing Bai, Ruiqiang Zhang, Karolina Buchner, Ciya Liao, and Fernando Diaz. [n.d.]. Towards Recency Ranking in Web Search. In WSDM 2010.

Google Scholar

[3]

Shan Jiang, Yuening Hu, Changsung Kang, Tim Daly, Jr., Dawei Yin, Yi Chang, and Chengxiang Zhai. [n.d.]. Learning Query and Document Relevance from a Web-scale Click Graph. In SIGIR 2016.

Digital Library

Google Scholar

[4]

Milad Shokouhi and Kira Radinsky. [n.d.]. Time-sensitive Query Auto-completion. In SIGIR 2012.

Google Scholar

[5]

Wei Wu, Hang Li, and Jun Xu. [n.d.]. Learning Query and Document Similarities from Click-through Bipartite Graph with Metadata. In WSDM 2013.

Digital Library

Google Scholar

Cited By

View all

Yang THan CLuo CGupta PPhillips JAi QChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes ApproachProceedings of the ACM Web Conference 202410.1145/3589334.3645487(1486-1496)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645487
Xv GLin CGuan WGou JLi XDeng HXu JZheng BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)E-commerce Search via Content Collaborative Graph Neural NetworkProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599320(2885-2897)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599320
Musgrave PHan CGupta PChen HDuh WHuang HKato MMothe JPoblete B(2023)Measuring Service-Level Learning Effects in Search Via Query-Randomized ExperimentsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592020(2169-2173)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592020
Show More Cited By

Index Terms

Treating Cold Start in Product Search by Priors
1. Information systems
  1. Information retrieval

Index terms have been assigned to the content through auto-classification.

Recommendations

Addressing Cold Start in Product Search via Empirical Bayes
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Cold start is a challenge in product search. Profuse literature addresses related problems such as bias and diversity in search, and cold start is a classic topic in recommender systems research. While search cold start might be seen conceptually as a ...
Diversifying product search results
SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

In recent years, online shopping is becoming more and more popular. Users type keyword queries on product search systems to find relevant products, accessories, and even related products. However, existing product search systems always return very ...
Enhancing product search by best-selling prediction in e-commerce
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

With the rapid growth of E-Commerce on the Internet, online product search service has emerged as a popular and effective paradigm for customers to find desired products and select transactions. Most product search engines today are based on adaptations ...

Comments

Information & Contributors

Information

Published In

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WWW '20

Sponsor:

SIGWEB

WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
142
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Yang THan CLuo CGupta PPhillips JAi QChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes ApproachProceedings of the ACM Web Conference 202410.1145/3589334.3645487(1486-1496)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645487
Xv GLin CGuan WGou JLi XDeng HXu JZheng BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)E-commerce Search via Content Collaborative Graph Neural NetworkProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599320(2885-2897)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599320
Musgrave PHan CGupta PChen HDuh WHuang HKato MMothe JPoblete B(2023)Measuring Service-Level Learning Effects in Search Via Query-Randomized ExperimentsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592020(2169-2173)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592020
Han CCastells PGupta PXu XSalaka VAl Hasan MXiong L(2022)Addressing Cold Start in Product Search via Empirical BayesProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557066(3141-3151)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557066
Yang TLuo CLu HGupta PYin BAi QAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Can Clicks Be Both Labels and Features?Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531948(6-17)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531948

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Abstract

References

Cited By

Index Terms

Recommendations

Addressing Cold Start in Product Search via Empirical Bayes

Diversifying product search results

Enhancing product search by best-selling prediction in e-commerce

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

HTML Format

Share

Share this Publication link

Share on social media

Affiliations