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EditKG: Editing Knowledge Graph for Recommendation

Published: 11 July 2024 Publication History

Abstract

With the enrichment of user-item interactions, Graph Neural Networks (GNNs) are widely used in recommender systems to alleviate information overload. Nevertheless, they still suffer from the cold-start issue. Knowledge Graphs (KGs), providing external information, have been extensively applied in GNN-based methods to mitigate this issue. However, current KG-aware recommendation methods suffer from the knowledge imbalance problem caused by incompleteness of existing KGs. This imbalance is reflected by the long-tail phenomenon of item attributes, i.e., unpopular items usually lack more attributes compared to popular items. To tackle this problem, we propose a novel framework called EditKG: Editing Knowledge Graph for Recommendation, to balance attribute distribution of items via editing KGs. EditKG consists of two key designs: Knowledge Generator and Knowledge Deleter. Knowledge Generator generates attributes for items by exploring their mutual information correlations and semantic correlations. Knowledge Deleter removes the task-irrelevant item attributes according to the parameterized task relevance score, while dropping the spurious item attributes through aligning the attribute scores. Extensive experiments on three benchmark datasets demonstrate that EditKG significantly outperforms state-of-the-art methods, and achieves 8.98% average improvement. The implementations are available at https://github.com/gutang-97/2024SIGIR-EditKG.

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Cited By

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  • (2025)TGformer: A Graph Transformer Framework for Knowledge Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348674737:1(526-541)Online publication date: Jan-2025

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    cover image ACM Conferences
    SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2024
    3164 pages
    ISBN:9798400704314
    DOI:10.1145/3626772
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    Published: 11 July 2024

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    Author Tags

    1. graph neural network
    2. knowledge graph
    3. knowledge imbalance
    4. recommendation

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    • NSF China
    • Shanghai Pilot Program for Basic Research - Shanghai Jiao Tong University
    • National Key R&D Program of China

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    • (2025)TGformer: A Graph Transformer Framework for Knowledge Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348674737:1(526-541)Online publication date: Jan-2025

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