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 language | English |
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Title of host publication | Proceedings of the 30th ACM International Conference on Information and Knowledge Management, CIKM '21 |
Place of Publication | New York |
Publisher | Association for Computing Machinery |
Pages | 4721-4724 |
ISBN (Electronic) | 9781450384469 |
DOIs | |
Publication status | Published - 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/10.1145/3459637.3481979Keywords
- Hashtags
- Micro-Video
- E-commerce
- Deep learning
- Graph Representation