# Micro-video Tagging via Jointly Modeling Social Influence and Tag Relation

arXiv: https://arxiv.org/abs/2303.08318 · HTML: https://mlsanity.com/paper/2303.08318/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [Efficient Low-rank Multimodal Fusion with Modality-Specific Factors](https://mlsanity.com/paper/1806.00064/) (2018)
- [Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks](https://mlsanity.com/paper/2112.14936/) (2021)
- [Cross-modality Person re-identification with Shared-Specific Feature Transfer](https://mlsanity.com/paper/2002.12489/) (2020)
- [Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification](https://mlsanity.com/paper/1912.07872/) (2019)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Multi-Label Classification with Label Graph Superimposing](https://mlsanity.com/paper/1911.09243/) (2019)
- [GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback](https://mlsanity.com/paper/2111.02036/) (2021)
- [NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification](https://mlsanity.com/paper/1811.05014/) (2018)
- [Adversarial Multimodal Representation Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2003.07162/) (2020)
- [Capturing Global Informativeness in Open Domain Keyphrase Extraction](https://mlsanity.com/paper/2004.13639/) (2020)
- [Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach](https://mlsanity.com/paper/2008.11567/) (2020)
- [A User-Centered Concept Mining System for Query and Document Understanding at Tencent](https://mlsanity.com/paper/1905.08487/) (2019)
- [Personalized Hashtag Recommendation for Micro-videos](https://mlsanity.com/paper/1908.09987/) (2019)
- [Time-sync Video Tag Extraction Using Semantic Association Graph](https://mlsanity.com/paper/1905.01053/) (2019)

## Built on by (most cited first, 3 of 3)

- [Temporal Sentence Grounding in Streaming Videos](https://mlsanity.com/paper/2308.07102/) (2023)
- [RTQ: Rethinking Video-language Understanding Based on Image-text Model](https://mlsanity.com/paper/2312.00347/) (2023)
- [From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework](https://mlsanity.com/paper/2605.02939/) (2026)

## Abstract (arXiv)

The last decade has witnessed the proliferation of micro-videos on various user-generated content platforms. According to our statistics, around 85.7% of micro-videos lack annotation. In this paper, we focus on annotating micro-videos with tags. Existing methods mostly focus on analyzing video content, neglecting users' social influence and tag relation. Meanwhile, existing tag relation construction methods suffer from either deficient performance or low tag coverage. To jointly model social influence and tag relation, we formulate micro-video tagging as a link prediction problem in a constructed heterogeneous network. Specifically, the tag relation (represented by tag ontology) is constructed in a semi-supervised manner. Then, we combine tag relation, video-tag annotation, and user-follow relation to build the network. Afterward, a better video and tag representation are derived through Behavior Spread modeling and visual and linguistic knowledge aggregation. Finally, the semantic similarity between each micro-video and all candidate tags is calculated in this video-tag network. Extensive experiments on industrial datasets of three verticals verify the superiority of our model compared with several state-of-the-art baselines.