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Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network

Published: 17 October 2018 Publication History

Abstract

Since heterogeneous information network (HIN) is able to integrate complex information and contain rich semantics, there is a surge of HIN based recommendation in recent years. Although existing methods have achieved performance improvement to some extent, they still face the following problems: how to extensively exploit and comprehensively explore the local and global information in HIN for recommendation. To address these issues, we propose a unified model LGRec to fuse local and global information for top-N recommendation in HIN. We firstly model most informative local neighbor information for users and items respectively with a co-attention mechanism. In addition, our model learns effective relation representations between users and items to capture rich information in HIN by optimizing a multi-label classification problem. Finally, we combine the two parts into an unified model for top-N recommendation. Extensive experiments on four real-world datasets demonstrate the effectiveness of the proposed model.

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  • (2024)M-scan: A Multi-Scenario Causal-driven Adaptive Network for RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645635(3844-3853)Online publication date: 13-May-2024
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  • (2024)Meta-path automatically extracted from heterogeneous information network for recommendationWorld Wide Web10.1007/s11280-024-01265-427:3Online publication date: 13-Apr-2024
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cover image ACM Conferences
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
October 2018
2362 pages
ISBN:9781450360142
DOI:10.1145/3269206
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 October 2018

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Author Tags

  1. attention mechanism
  2. heterogeneous information network
  3. local and global information
  4. recommender system

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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  • (2024)M-scan: A Multi-Scenario Causal-driven Adaptive Network for RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645635(3844-3853)Online publication date: 13-May-2024
  • (2024)Dynamic Recommendation Based on Graph Diffusion and Ebbinghaus CurveIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.326761111:2(2755-2764)Online publication date: Apr-2024
  • (2024)Meta-path automatically extracted from heterogeneous information network for recommendationWorld Wide Web10.1007/s11280-024-01265-427:3Online publication date: 13-Apr-2024
  • (2023)Explainable Meta-Path Based Recommender SystemsACM Transactions on Recommender Systems10.1145/36258283:2(1-28)Online publication date: 28-Sep-2023
  • (2023)AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614773(976-986)Online publication date: 21-Oct-2023
  • (2023)Reinforced MOOCs Concept Recommendation in Heterogeneous Information NetworksACM Transactions on the Web10.1145/358051017:3(1-27)Online publication date: 22-May-2023
  • (2023)L-BGNN: Layerwise Trained Bipartite Graph Neural NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.317119934:12(10711-10723)Online publication date: Dec-2023
  • (2023)A crowd-sourcing recommendation algorithm OPCA-CF using outer-product co-attention mechanismNondestructive Testing and Evaluation10.1080/10589759.2023.227352539:1(102-124)Online publication date: 3-Nov-2023
  • (2023)Incorporating metapath interaction on heterogeneous information network for social recommendationFrontiers of Computer Science10.1007/s11704-022-2438-118:1Online publication date: 12-Aug-2023
  • (2022)Improving Bandit Learning Via Heterogeneous Information Networks: Algorithms and ApplicationsACM Transactions on Knowledge Discovery from Data10.1145/352259016:6(1-25)Online publication date: 30-Jul-2022
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