Personalized hashtag recommendation for micro-videos

Yinwei Wei, Zhiyong Cheng, Xuzheng Yu, Zhou Zhao, Lei Zhu, Liqiang Nie

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

55 Citations (Scopus)

Abstract

Personalized hashtag recommendation methods aim to suggest users hashtags to annotate, categorize, and describe their posts. The hashtags, that a user provides to a post (e.g., a micro-video), are the ones which in her mind can well describe the post content where she is interested in. It means that we should consider both users' preferences on the post contents and their personal understanding on the hashtags. Most existing methods rely on modeling either the interactions between hashtags and posts or the interactions between users and hashtags for hashtag recommendation. These methods have not well explored the complicated interactions among users, hashtags, and micro-videos. In this paper, towards the personalized micro-video hashtag recommendation, we propose a Graph Convolution Network based Personalized Hashtag Recommendation (GCN-PHR) model, which leverages recently advanced GCN techniques to model the complicate interactions among <users, hashtags, micro-videos> and learn their representations. In our model, the users, hashtags, and micro-videos are three types of nodes in a graph and they are linked based on their direct associations. In particular, the message-passing strategy is used to learn the representation of a node (e.g., user) by aggregating the message passed from the directly linked other types of nodes (e.g., hashtag and micro-video). Because a user is often only interested in certain parts of a micro-video and a hashtag is typically used to describe the part (of a micro-video) that the user is interested in, we leverage the attention mechanism to filter the message passed from micro-videos to users and hashtags, which can significantly improve the representation capability. Extensive experiments have been conducted on two real-world micro-video datasets and demonstrate that our model outperforms the state-of-the-art approaches by a large margin.

Original languageEnglish
Title of host publicationProceedings of the 27th ACM International Conference on Multimedia
EditorsGuillaume Gravier, Hayley Hung, Chong-Wah Ngo, Wei Tsang Ooi
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1446-1454
Number of pages9
ISBN (Electronic)9781450368896, 9781450367936
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventACM International Conference on Multimedia 2019 - Nice, France
Duration: 21 Oct 201925 Oct 2019
Conference number: 27th
https://dl.acm.org/doi/proceedings/10.1145/3343031

Conference

ConferenceACM International Conference on Multimedia 2019
Abbreviated titleMM 2019
Country/TerritoryFrance
CityNice
Period21/10/1925/10/19
Internet address

Keywords

  • Graph Neural Network
  • Hashtag Recommendation
  • Micro-video Understanding
  • Personalization

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