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Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation

Published: 30 October 2021 Publication History

Abstract

Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relationships are not helpful for a target user-item prediction. To exploit the knowledge graph to capture target-specific knowledge relationships in recommender systems, we need to distill the knowledge graph to reserve the useful information and refine the knowledge to capture the users' preferences. To address the issues, we propose Knowledge-aware Conditional Attention Networks (KCAN), which is an end-to-end model to incorporate knowledge graph into a recommender system. Specifically, we use a knowledge-aware attention propagation manner to obtain the node representation first, which captures the global semantic similarity on the user-item network and the knowledge graph. Then given a target, i.e., a user-item pair, we automatically distill the knowledge graph into the target-specific subgraph based on the knowledge-aware attention. Afterward, by applying a conditional attention aggregation on the subgraph, we refine the knowledge graph to obtain target-specific node representations. Therefore, we can gain both representability and personalization to achieve overall performance. Experimental results on real-world datasets demonstrate the effectiveness of our framework over the state-of-the-art algorithms.

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Cited By

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  • (2024)Social Perception with Graph Attention Network for RecommendationACM Transactions on Recommender Systems10.1145/3665503Online publication date: 21-May-2024
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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. conditional attention
    2. graph convolutional network
    3. knowledge graph
    4. network representation learning

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    • (2024)Social Perception with Graph Attention Network for RecommendationACM Transactions on Recommender Systems10.1145/3665503Online publication date: 21-May-2024
    • (2024)KGCNA: Knowledge Graph Collaborative Neighbor Awareness Network for RecommendationIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33699768:4(2736-2748)Online publication date: Aug-2024
    • (2024)Community Enhanced Knowledge Graph for RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.338360311:5(5789-5802)Online publication date: Oct-2024
    • (2024)Leveraging Hyperbolic Dynamic Neural Networks for Knowledge-Aware RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.335346711:3(4396-4411)Online publication date: Jun-2024
    • (2024)A Comprehensive Survey on Graph Summarization With Graph Neural NetworksIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33505455:8(3780-3800)Online publication date: Aug-2024
    • (2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: 8-Apr-2024
    • (2024)Knowledge-aware fine-grained attention networks with refined knowledge graph embedding for personalized recommendationExpert Systems with Applications10.1016/j.eswa.2024.123710249(123710)Online publication date: Sep-2024
    • (2024)DEKGCI: A double-ended recommendation model for integrating knowledge graph and user–item interaction graphThe Journal of Supercomputing10.1007/s11227-024-06344-xOnline publication date: 8-Jul-2024
    • (2023)Author Name Disambiguation based on Capsule Network via Semantic and Structural FeaturesProceedings of the 2023 6th International Conference on Signal Processing and Machine Learning10.1145/3614008.3614053(293-300)Online publication date: 14-Jul-2023
    • (2023)Reciprocal Sequential RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608798(89-100)Online publication date: 14-Sep-2023
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