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A Knowledge-Aware Recommender with Attention-Enhanced Dynamic Convolutional Network

Published: 30 October 2021 Publication History

Abstract

Sequential recommendation systems seek to learn users' preferences to predict their next actions based on the items engaged recently. Static behavior of users requires a long time to form, but short-term interactions with items usually meet some actual needs in reality and are more variable. RNN-based models are always constrained by the strong order assumption and are hard to model the complex and changeable data flexibly. Most of the CNN-based models are limited to the fixed convolutional kernel. All these methods are suboptimal when modeling the dynamics of item-to-item transitions. It is difficult to describe the items with complex relations and extract the fine-grained user preferences from the interaction sequence. To address these issues, we propose a knowledge-aware sequential recommender with the attention-enhanced dynamic convolutional network (KAeDCN). Our model combines the dynamic convolutional network with attention mechanisms to capture changing dependencies in the sequence. Meanwhile, we enhance the representations of items with Knowledge Graph (KG) information through an information fusion module to capture the fine-grained user preferences. The experiments on four public datasets demonstrate that KAeDCN outperforms most of the state-of-the-art sequential recommenders. Furthermore, experimental results also prove that KAeDCN can enhance the representations of items effectively and improve the extractability of sequential dependencies.

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  • (2023)News Recommendation Based on User Topic and Entity Preferences in Historical BehaviorInformation10.3390/info1402006014:2(60)Online publication date: 18-Jan-2023
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    cover image ACM Conferences
    CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
    October 2021
    4966 pages
    ISBN:9781450384469
    DOI:10.1145/3459637
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    Published: 30 October 2021

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    Author Tags

    1. attention mechanism
    2. dynamic convolutions
    3. knowledge graph
    4. sequential recommendation

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    • (2023)News Recommendation Based on User Topic and Entity Preferences in Historical BehaviorInformation10.3390/info1402006014:2(60)Online publication date: 18-Jan-2023
    • (2023)Enhancing sequential recommendation with contrastive Generative Adversarial NetworkInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10333160:3Online publication date: 1-May-2023
    • (2023)Knowledge graph-based recommendation method for cold chain logisticsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120230227:COnline publication date: 11-Jul-2023
    • (2023)Temporal-Aware Multi-behavior Contrastive RecommendationDatabase Systems for Advanced Applications10.1007/978-3-031-30672-3_18(269-285)Online publication date: 17-Apr-2023
    • (2022)CPGCN: Collaborative Property-aware Graph Convolutional Networks for Service Recommendation2022 IEEE International Conference on Services Computing (SCC)10.1109/SCC55611.2022.00016(10-19)Online publication date: Jul-2022
    • (2022)Time Interval Aware Collaborative Sequential Recommendation with Self-supervised LearningWeb and Big Data10.1007/978-3-031-25201-3_7(87-101)Online publication date: 11-Aug-2022
    • (2022)NED-GNN: Detecting and Dropping Noisy Edges in Graph Neural NetworksWeb and Big Data10.1007/978-3-031-25158-0_8(91-105)Online publication date: 11-Aug-2022
    • (2022)GISDCN: A Graph-Based Interpolation Sequential Recommender with Deformable Convolutional NetworkDatabase Systems for Advanced Applications10.1007/978-3-031-00126-0_21(289-297)Online publication date: 11-Apr-2022

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