Mining Tag Relationships in CQA Sites

Suryamukhi, K.; Vivekananda, P. D.; Singh, Manish

doi:10.1007/978-3-030-89022-3_27

Cited by 2 publications

(1 citation statement)

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“…As illustrated in Fig. 5, we measured the construction performance of subtopic discovery, and our method outperforms the rule-based method FBVO [6], unsupervised method OntoJob [29] and OntoCQA [26] in a clear margin. Our constructed ontology covers 96.67% of tags, and only 2.94% of tags are covered through 𝜖 𝑟 relaxation.…”

Section: Training and Inferencementioning

confidence: 95%

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

Wang,

Gan,

Wei

et al. 2023

Preprint

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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.

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Section: Training and Inferencementioning

confidence: 95%