Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations

• URL: http://arxiv.org/abs/2107.05235v1
• Date: Mon, 12 Jul 2021 07:34:20 GMT
• Title: Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations
• Authors: Liwei Huang, Yutao Ma, Yanbo Liu, Shuliang Wang, Deyi Li
• Abstract summary: We propose a new deep learning-based sequential recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation. It realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions.
• Score: 3.286961611175469
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most of the existing deep learning-based sequential recommendation approaches utilize the recurrent neural network architecture or self-attention to model the sequential patterns and temporal influence among a user's historical behavior and learn the user's preference at a specific time. However, these methods have two main drawbacks. First, they focus on modeling users' dynamic states from a user-centric perspective and always neglect the dynamics of items over time. Second, most of them deal with only the first-order user-item interactions and do not consider the high-order connectivity between users and items, which has recently been proved helpful for the sequential recommendation. To address the above problems, in this article, we attempt to model user-item interactions by a bipartite graph structure and propose a new recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) for the sequential recommendation. PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator. Also, it realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions. To demonstrate the effectiveness of PTGCN, we carried out a comprehensive evaluation of PTGCN on three real-world datasets of different sizes compared with a few competitive baselines. Experimental results indicate that PTGCN outperforms several state-of-the-art models in terms of two commonly-used evaluation metrics for ranking.

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