TagPick: A system for bridging micro-video hashtags and e-commerce categories

Li HE, Dingxian WANG, Hanzhang WANG, Hongxu CHEN, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

3 Citations (Scopus)

Abstract

Hashtag, a product of user tagging behavior, which can well describe the semantics of the user-generated content personally over social network applications, e.g., the recently popular micro-videos. Hashtags have been widely used to facilitate various micro-video retrieval scenarios, such as search engine and categorization. In order to leverage hashtags on micro-media platform for effective e-commerce marketing campaign, there is a demand from e-commerce industry to develop a mapping algorithm bridging its categories and micro-video hashtags. In this demo paper, we therefore proposed a novel solution called TagPick that incorporates clues from all user behavior metadata (hashtags, interactions, multimedia information) as well as relational data (graph-based network) into a unified system to reveal the correlation between e-commerce categories and hashtags in industrial scenarios. In particular, we provide a tag-level popularity strategy to recommend the relevant hashtags for e-Commerce platform (e.g., eBay). Copyright © 2021 Association for Computing Machinery.

Original languageEnglish
Title of host publicationProceedings of the 30th ACM International Conference on Information and Knowledge Management, CIKM '21
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages4721-4724
ISBN (Electronic)9781450384469
DOIs
Publication statusPublished - Oct 2021

Citation

He, L., Wang, D., Wang, H., Chen, H., & Xu, G. (2021). TagPick: A system for bridging micro-video hashtags and e-commerce categories. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management, CIKM '21 (pp. 4721-4724). Association for Computing Machinery. https://doi.org/10.1145/3459637.3481979

Keywords

  • Hashtags
  • Micro-Video
  • E-commerce
  • Deep learning
  • Graph Representation

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