skip to main content
10.1145/3543873.3587300acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
poster

A ML-based Approach for HTML-based Style Recommendation

Published: 30 April 2023 Publication History

Abstract

Given a large corpus of HTML-based emails (or websites, posters, documents) collected from the web, how can we train a model capable of learning from such rich heterogeneous data for HTML-based style recommendation tasks such as recommending useful design styles or suggesting alternative HTML designs? To address this new learning task, we first decompose each HTML document in the corpus into a sequence of smaller HTML fragments where each fragment may consist of a set of HTML entities such as buttons, images, textual content (titles, paragraphs) and stylistic entities such as background-style, font-style, button-style, among others. From these HTML fragments, we then derive a single large heterogeneous hypergraph that captures the higher-order dependencies between HTML fragments and entities in such fragments, both within the same HTML document as well as across the HTML documents in the corpus. We then formulate this new HTML style recommendation task as a hypergraph representation learning problem and propose an approach to solve it. Our approach is able to learn effective low-dimensional representations of the higher-order fragments that consist of sets of heterogeneous entities as well as low-dimensional representations of the individual entities themselves. We demonstrate the effectiveness of the approach across several design style recommendation tasks. To the best of our knowledge, this work is the first to develop an ML-based model for the task of HTML-based email style recommendation.

References

[1]
2022. ActiveCampaign - Email Marketing, Automation, and CRM. https://www.activecampaign.com
[2]
2022. Mailchimp: Marketing smarts for big ideas. https://mailchimp.com/
[3]
2022. Moosend - Email Marketing Automation Platform for Thriving Businesses. https://moosend.com/
[4]
2022. Sendinblue - All Your Digital Marketing Tools in One Place. https://www.sendinblue.com
[5]
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. 2019. Hypergraph neural networks. In AAAI, Vol. 33. 3558–3565.
[6]
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016).
[7]
Fayokemi Ojo, Ryan A Rossi, Jane Hoffswell, Shunan Guo, Fan Du, Sungchul Kim, Chang Xiao, and Eunyee Koh. 2022. VisGNN: Personalized Visualization Recommendation via Graph Neural Networks. In WWW. 2810–2818.
[8]
Qifan Wang, Yi Fang, Anirudh Ravula, Fuli Feng, Xiaojun Quan, and Dongfang Liu. 2022. WebFormer: The Web-Page Transformer for Structure Information Extraction. In WWW. 3124–3133.
[9]
Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar. 2019. HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. In NeurIPS. 1509–1520.
[10]
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In KDD. 974–983.

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023
April 2023
1567 pages
ISBN:9781450394192
DOI:10.1145/3543873
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2023

Check for updates

Author Tags

  1. Design style recommendation
  2. graph neural networks
  3. hypergraphs

Qualifiers

  • Poster
  • Research
  • Refereed limited

Conference

WWW '23
Sponsor:
WWW '23: The ACM Web Conference 2023
April 30 - May 4, 2023
TX, Austin, USA

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 202
    Total Downloads
  • Downloads (Last 12 months)53
  • Downloads (Last 6 weeks)0
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

View Options

Login options

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

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media