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Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-Video Recommendation

arXiv 2501.02666 ↗First posted 5 Jan 2025cs.IR cs.AIcited by 0

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Abstract (arXiv)

Micro-video recommendation is attracting global attention and becoming a popular daily service for people of all ages. Recently, Graph Neural Networks-based micro-video recommendation has displayed performance improvement for many kinds of recommendation tasks. However, the existing works fail to fully consider the characteristics of micro-videos, such as the high timeliness of news nature micro-video recommendation and sequential interactions of frequently changed interests. In this paper, a novel Multi-aggregator Time-warping Heterogeneous Graph Neural Network (MTHGNN) is proposed for personalized news nature micro-video recommendation based on sequential sessions, where characteristics of micro-videos are comprehensively studied, users' preference is mined via multi-aggregator, the temporal and dynamic changes of users' preference are captured, and timeliness is considered. Through the comparison with the state-of-the-arts, the experimental results validate the superiority of our MTHGNN model.

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References · 16

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4 more
  1. 2021Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendationcited by 18
  2. 2021RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendationcited by 15
  3. 2021Multi-trends Enhanced Dynamic Micro-video Recommendationcited by 5
  4. 2021Modeling High-order Interactions across Multi-interests for Micro-video Recommendationcited by 1

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