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HOP-rec: high-order proximity for implicit recommendation

Published: 27 September 2018 Publication History

Abstract

Recommender systems are vital ingredients for many e-commerce services. In the literature, two of the most popular approaches are based on factorization and graph-based models; the former approach captures user preferences by factorizing the observed direct interactions between users and items, and the latter extracts indirect preferences from the graphs constructed by user-item interactions. In this paper we present HOP-Rec, a unified and efficient method that incorporates the two approaches. The proposed method involves random surfing on a graph to harvest high-order information among neighborhood items for each user. Instead of factorizing a transition matrix, our method introduces a confidence weighting parameter to simulate all high-order information simultaneously, for which we maintain a sparse user-item interaction matrix and enrich the matrix for each user using random walks. Experimental results show that our approach significantly outperforms the state of the art on a range of large-scale real-world datasets.

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References

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  • (2024)Explicitly Exploiting Implicit User and Item Relations in Graph Convolutional Network (GCN) for RecommendationElectronics10.3390/electronics1314281113:14(2811)Online publication date: 17-Jul-2024
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cover image ACM Conferences
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
September 2018
600 pages
ISBN:9781450359016
DOI:10.1145/3240323
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 the author(s) 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: 27 September 2018

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Author Tags

  1. bipartite graph
  2. collaborative filtering
  3. implicit feedback
  4. matrix factorization
  5. random walks
  6. top-N recommendation

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RecSys '18
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RecSys '18: Twelfth ACM Conference on Recommender Systems
October 2, 2018
British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2025)Graph-Based Feature Crossing to Enhance Recommender SystemsMathematics10.3390/math1302030213:2(302)Online publication date: 18-Jan-2025
  • (2025)Spatial Craving Patterns in Marijuana Users: Insights From fMRI Brain Connectivity Analysis With High-Order Graph Attention Neural NetworksIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.346237129:1(358-370)Online publication date: Jan-2025
  • (2024)Explicitly Exploiting Implicit User and Item Relations in Graph Convolutional Network (GCN) for RecommendationElectronics10.3390/electronics1314281113:14(2811)Online publication date: 17-Jul-2024
  • (2024)Rolling Forward: Enhancing LightGCN with Causal Graph Convolution for Credit Bond RecommendationProceedings of the 5th ACM International Conference on AI in Finance10.1145/3677052.3698683(231-238)Online publication date: 14-Nov-2024
  • (2024)Improving Graph Collaborative Filtering with Directional Behavior Enhanced Contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/366357418:8(1-20)Online publication date: 2-May-2024
  • (2024)Integrating Matrix Factorization with Graph based ModelsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688013(1314-1317)Online publication date: 8-Oct-2024
  • (2024)Unifying Graph Convolution and Contrastive Learning in Collaborative FilteringProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671840(3425-3436)Online publication date: 25-Aug-2024
  • (2024)Content-based Graph Reconstruction for Cold-start Item RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657801(1263-1273)Online publication date: 10-Jul-2024
  • (2024)Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645371(3253-3264)Online publication date: 13-May-2024
  • (2024)Learning Neighbor User Intention on User–Item Interaction Graphs for Better Sequential RecommendationACM Transactions on the Web10.1145/358052018:2(1-28)Online publication date: 8-Jan-2024
  • Show More Cited By

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