A ML-based Approach for HTML-based Style Recommendation
Pages 9 - 13
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.
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- A ML-based Approach for HTML-based Style Recommendation
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April 2023
1567 pages
ISBN:9781450394192
DOI:10.1145/3543873
Copyright © 2023 Owner/Author.
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.
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Association for Computing Machinery
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Published: 30 April 2023
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