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

• URL: http://arxiv.org/abs/2501.02666v1
• Date: Sun, 05 Jan 2025 21:14:35 GMT
• Title: Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-Video Recommendation
• Authors: Jinkun Han, Wei Li, Xhipeng Cai, Yingshu Li,
• Abstract summary: Graph Neural Networks-based micro-video recommendation has displayed performance improvement for many kinds of recommendation tasks. In this paper, a novel Multi-aggregator Time-warping Heterogeneous Graph Neural Network (MTHGNN) is proposed for personalized news nature micro-video recommendation.
• Score: 3.0734655107038713
• License:
• Abstract: 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.

Related papers

• Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation [97.82707398481273]
  We develop a novel meta-learning-based multimodal fusion framework called Meta Multimodal Fusion (MetaMMF) Based on the meta information extracted from the multimodal features of the input task, MetaMMF parameterizes a neural network as the item-specific fusion function via a meta learner. We perform extensive experiments on three benchmark datasets, demonstrating the significant improvements over several state-of-the-art multimodal recommendation models.
  arXiv  Detail & Related papers  (2025-01-13T07:51:43Z)
• Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching [43.79560010763052]
  We propose a model named OPAL for micro-video matching. OPAL elicits a user's multiple heterogeneous interests by disentangling multiple soft and hard interest embeddings. OPAL outperforms six state-of-the-art models in terms of recall and hit rate.
  arXiv  Detail & Related papers  (2024-07-20T03:41:57Z)
• Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition [48.21696443824074]
  We propose a novel framework for micro-expression recognition, named the Adaptive Temporal Motion Guided Graph Convolution Network (ATM-GCN) Our framework excels at capturing temporal dependencies between frames across the entire clip, thereby enhancing micro-expression recognition at the clip level.
  arXiv  Detail & Related papers  (2024-06-13T10:57:24Z)
• A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation [7.54949302096348]
  We propose a vlogger-augmented graph neural network model VA-GNN, which takes the effect of vloggers into consideration. Specifically, we construct a tripartite graph with users, micro-videos, and vloggers as nodes, capturing user preferences from different views. When predicting the next user-video interaction, we adaptively combine the user preferences for a video itself and its vlogger.
  arXiv  Detail & Related papers  (2024-05-28T15:13:29Z)
• No-frills Temporal Video Grounding: Multi-Scale Neighboring Attention and Zoom-in Boundary Detection [52.03562682785128]
  Temporal video grounding aims to retrieve the time interval of a language query from an untrimmed video. A significant challenge in TVG is the low "Semantic Noise Ratio (SNR)", which results in worse performance with lower SNR. We propose a no-frills TVG model that consists of two core modules, namely multi-scale neighboring attention and zoom-in boundary detection.
  arXiv  Detail & Related papers  (2023-07-20T04:12:10Z)
• Multi-queue Momentum Contrast for Microvideo-Product Retrieval [57.527227171945796]
  We formulate the microvideo-product retrieval task, which is the first attempt to explore the retrieval between the multi-modal and multi-modal instances. A novel approach named Multi-Queue Momentum Contrast (MQMC) network is proposed for bidirectional retrieval. A discriminative selection strategy with a multi-queue is used to distinguish the importance of different negatives based on their categories.
  arXiv  Detail & Related papers  (2022-12-22T03:47:14Z)
• Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation [30.67251766249372]
  We propose a novel concept-aware denoising graph neural network (named CONDE) for micro-video recommendation. The proposed CONDE achieves significantly better recommendation performance than the existing state-of-the-art solutions.
  arXiv  Detail & Related papers  (2021-09-28T07:02:52Z)
• Modeling High-order Interactions across Multi-interests for Micro-video Reommendation [65.16624625748068]
  We propose a Self-over-Co Attention module to enhance user's interest representation. In particular, we first use co-attention to model correlation patterns across different levels and then use self-attention to model correlation patterns within a specific level.
  arXiv  Detail & Related papers  (2021-04-01T07:20:15Z)
• Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework [54.194340961353944]
  We propose a multimodal variational encoder-decoder framework for micro-video popularity tasks. MMVED learns a prediction embedding of a micro-video that is informative to its popularity level. Experiments conducted on a public dataset and a dataset we collect from Xigua demonstrate the effectiveness of the proposed MMVED framework.
  arXiv  Detail & Related papers  (2020-03-28T06:08:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.