Cited By
View all- He LXu GJameel SWang XChen H(2023)Graph-Aware Deep Fusion Networks for Online Spam Review DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318981310:5(2557-2565)Online publication date: Oct-2023
TagPick: A System for Bridging Micro-Video Hashtags and E-commerce Categories
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).
Supplementary Material
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).
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References
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Da Cao, Lianhai Miao, Huigui Rong, Zheng Qin, and Liqiang Nie. 2020. Hashtag our stories: Hashtag recommendation for micro-videos via harnessing multiple modalities. Knowledge-Based Systems, Vol. 203 (2020), 106114.
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Index Terms
- TagPick: A System for Bridging Micro-Video Hashtags and E-commerce Categories
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Published In
October 2021
4966 pages
ISBN:9781450384469
DOI:10.1145/3459637
- General Chairs:
- Gianluca Demartini,
- Guido Zuccon,
- Program Chairs:
- J. Shane Culpepper,
- Zi Huang,
- Hanghang Tong
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Published: 30 October 2021
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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management
November 1 - 5, 2021
Queensland, Virtual Event, Australia
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View all- He LXu GJameel SWang XChen H(2023)Graph-Aware Deep Fusion Networks for Online Spam Review DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318981310:5(2557-2565)Online publication date: Oct-2023
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