research-article

Knowledge-refined Denoising Network for Robust Recommendation

Authors:

Yunjun GaoAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 362 - 371

https://doi.org/10.1145/3539618.3591707

Published: 18 July 2023 Publication History

Abstract

Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing knowledge-aware recommendation methods directly perform information propagation on KG and user-item bipartite graph, ignoring the impacts of task-irrelevant knowledge propagation and vulnerability to interaction noise, which limits their performance. To solve these issues, we propose a robust knowledge-aware recommendation framework, called Knowledge-refined Denoising Network (KRDN), to prune the task-irrelevant knowledge associations and noisy implicit feedback simultaneously. KRDN consists of an adaptive knowledge refining strategy and a contrastive denoising mechanism, which are able to automatically distill high-quality KG triplets for aggregation and prune noisy implicit feedback respectively. Besides, we also design the self-adapted loss function and the gradient estimator for model optimization. The experimental results on three benchmark datasets demonstrate the effectiveness and robustness of KRDN over the state-of-the-art knowledge-aware methods like KGIN, MCCLK, and KGCL, and also outperform robust recommendation models like SGL and SimGCL. The implementations are available at https://github.com/xj-zhu98/KRDN.

References

[1]

Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. Algorithms, Vol. 11, 9 (2018), 137--146.

[2]

Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013).

[3]

Yixin Cao, Xiang Wang, Xiangnan He, Zikun hu, and Tat-Seng Chua. 2019. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. In WWW. 151--161.

[4]

Huiyuan Chen, Lan Wang, Yusan Lin, Chin-Chia Michael Yeh, Fei Wang, and Hao Yang. 2021. Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems. In SIGIR. 614--623.

[5]

Wen Chen, Pipei Huang, Jiaming Xu, Xin Guo, Cheng Guo, Fei Sun, Chao Li, Andreas Pfadler, Huan Zhao, and Binqiang Zhao. 2019. POG: personalized outfit generation for fashion recommendation at Alibaba iFashion. In KDD. 2662--2670.

[6]

Zhe Dong, Andriy Mnih, and George Tucker. 2020. DisARM: An Antithetic Gradient Estimator for Binary Latent Variables. In NeurIPS. 18637--18647.

[7]

Yuntao Du, Jianxun Lian, Jing Yao, Xiting Wang, Mingqi Wu, Lu Chen, Yunjun Gao, and Xing Xie. 2023. Towards Explainable Collaborative Filtering with Taste Clusters Learning. In WWW.

[8]

Yuntao Du, Xinjun Zhu, Lu Chen, Ziquan Fang, and Yunjun Gao. 2022a. MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation. TKDE (2022).

[9]

Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng, and Yunjun Gao. 2022b. HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation. In SIGIR. 1390--1400.

Digital Library

[10]

Yufei Feng, Binbin Hu, Fuyu Lv, Qingwen Liu, Zhiqiang Zhang, and Wenwu Ou. 2020. ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation. In SIGIR. 2231--2240.

[11]

Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2012. Personalized ranking for non-uniformly sampled items. In Proceedings of KDD Cup 2011. PMLR, 231--247.

Digital Library

[12]

Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, and Baihua Zheng. 2022. Self-Guided Learning to Denoise for Robust Recommendation. In SIGIR. 1412--1422.

[13]

Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In International Conference on Artificial Intelligence and Statistics.

[14]

William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1025--1035.

[15]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR. 639--648.

[16]

Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S. Yu. 2018. Leveraging Meta-Path Based Context for Top- N Recommendation with A Neural Co-Attention Model. In KDD. 1531--1540.

[17]

Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In ICDM. 263--272.

[18]

Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical reparameterization with gumbel-softmax. In ICLR.

[19]

Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, and Alexander J. Smola. 2020. An Efficient Neighborhood-Based Interaction Model for Recommendation on Heterogeneous Graph. In KDD. 75--84.

[20]

Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul. 1999. An Introduction to Variational Methods for Graphical Models. Machine learning, Vol. 2, 37 (1999), 183--233.

[21]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. In ICLR.

[22]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR.

[23]

Yehuda Koren. 2008. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. In KDD. 426--434.

[24]

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning Entity and Relation Embeddings for Knowledge Graph Completion. In AAAI. 2181--2187.

[25]

Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022. Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning. In WWW. 2320--2329.

[26]

Christos Louizos, Max Welling, and Diederik P Kingma. 2017. Learning sparse neural networks through L_0 regularization. In ICLR.

[27]

Yuanfu Lu, Yuan Fang, and Chuan Shi. 2020. Meta-Learning on Heterogeneous Information Networks for Cold-Start Recommendation. In KDD. 1563--1573.

[28]

Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019. Disentangled graph convolutional networks. In ICML. PMLR, 4212--4221.

[29]

Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, and Xiuqiang He. 2021. SimpleX: A Simple and Strong Baseline for Collaborative Filtering. In CIKM. 1243--1252.

[30]

Yuqi Qin, Pengfei Wang, and Chenliang Li. 2021. The world is binary: Contrastive learning for denoising next basket recommendation. In SIGIR. 859--868.

[31]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI. 452--461.

Digital Library

[32]

Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang, and Wayne Xin Zhao. 2022. Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering. In SIGIR. 122--132.

[33]

Ke Tu, Peng Cui, Daixin Wang, Zhiqiang Zhang, Jun Zhou, Yuan Qi, and Wenwu Zhu. 2021. Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation. In CIKM. 1834--1843.

[34]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR.

[35]

Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018a. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. In CIKM. 417--426.

[36]

Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018b. DKN: Deep knowledge-aware network for news recommendation. In WWW. 1835--1844.

Digital Library

[37]

Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and Zhongyuan Wang. 2019d. Knowledge-Aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. In KDD. 968--977.

[38]

Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019 e. Knowledge Graph Convolutional Networks for Recommender Systems. In WWW. 3307--3313.

[39]

Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. 2021a. Denoising implicit feedback for recommendation. In WSDM. 373--381.

[40]

Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019a. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD. 950--958.

Digital Library

[41]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019b. Neural Graph Collaborative Filtering. In SIGIR. 165--174.

[42]

Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, and Tat-Seng Chua. 2021b. Learning Intents behind Interactions with Knowledge Graph for Recommendation. In WWW. 878--887.

[43]

Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. 2019c. Explainable Reasoning over Knowledge Graphs for Recommendation. In AAAI. 5329--5336.

[44]

Yu Wang, Xin Xin, Zaiqiao Meng, Joemon M Jose, Fuli Feng, and Xiangnan He. 2022. Learning Robust Recommenders through Cross-Model Agreement. In WWW. 2015--2025.

[45]

Ze Wang, Guangyan Lin, Huobin Tan, Qinghong Chen, and Xiyang Liu. 2020. CKAN: Collaborative Knowledge-Aware Attentive Network for Recommender Systems. In SIGIR. 219--228.

Digital Library

[46]

Zitai Wang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, and Qingming Huang. 2021c. Implicit Feedbacks are Not Always Favorable: Iterative Relabeled One-Class Collaborative Filtering against Noisy Interactions. In MM. 3070--3078.

[47]

Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2020. Graph-refined convolutional network for multimedia recommendation with implicit feedback. In Proceedings of the 28th ACM international conference on multimedia. 3541--3549.

Digital Library

[48]

Ronald J. Williams. 1992. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning. Machine learning, Vol. 8, 3--4 (1992), 229--256.

Digital Library

[49]

Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying Graph Convolutional Networks. In ICML. 6861--6871.

[50]

Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726--735.

[51]

Yikun Xian, Zuohui Fu, Shan Muthukrishnan, Gerard De Melo, and Yongfeng Zhang. 2019. Reinforcement knowledge graph reasoning for explainable recommendation. In SIGIR. 285--294.

[52]

Ruobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, and Leyu Lin. 2020. Deep Feedback Network for Recommendation. In IJCAI. 2519--2525.

[53]

Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022. Knowledge graph contrastive learning for recommendation. In SIGIR. 1434--1443.

[54]

Yonghui Yang, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2021. Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization. In SIGIR. 71--80.

[55]

Mingzhang Yin and Mingyuan Zhou. 2019. ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks. In ICLR.

[56]

Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022. Are graph augmentations necessary? simple graph contrastive learning for recommendation. In SIGIR. 1294--1303.

[57]

Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative Knowledge Base Embedding for Recommender Systems. In KDD. 353--362.

[58]

Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, and Xin Cao. 2022. Multi-level cross-view contrastive learning for knowledge-aware recommender system. In SIGIR. 1358--1368.

Cited By

Tang GGan XWang JLu BWu LFu LZhou CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)EditKG: Editing Knowledge Graph for RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657723(112-122)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657723

Index Terms

Knowledge-refined Denoising Network for Robust Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Knowledge Graph Contrastive Learning for Recommendation
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fruitful facts and inherent semantic relatedness among items. However, the ...
EditKG: Editing Knowledge Graph for Recommendation
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

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 ...
User-Oriented Interest Representation on Knowledge Graph for Long-Tail Recommendation
Advanced Data Mining and Applications
Abstract
Graph neural networks have demonstrated impressive performance in the field of recommender systems. However, existing graph neural network recommendation approaches are proficient in capturing users’ mainstream interests and recommending popular ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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].

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGIR '23

Sponsor:

SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
290
Total Downloads

Downloads (Last 12 months)259
Downloads (Last 6 weeks)21

Reflects downloads up to 09 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tang GGan XWang JLu BWu LFu LZhou CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)EditKG: Editing Knowledge Graph for RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657723(112-122)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657723

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents