TagPick: A System for Bridging Micro-Video Hashtags and E-commerce Categories

TL;DR

TagPick is a system that maps micro-video hashtags to e-commerce categories by integrating user behavior data and relational networks, enhancing targeted marketing and content categorization.

Contribution

It introduces a novel unified approach combining metadata and graph data to bridge hashtags and e-commerce categories for micro-media platforms.

Findings

Effective hashtag-category mapping demonstrated in industrial scenarios

Improved hashtag recommendation accuracy for e-commerce marketing

Integration of multimedia and relational data enhances relevance

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).