Cited By
View all- Tachioka Y(2024)User Knowledge Prompt for Sequential RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691714(1142-1146)Online publication date: 8-Oct-2024
Unleashing the Power of Knowledge Graph for Recommendation via Invariant Learning
Pages 3745 - 3755
Abstract
Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of recommender systems. Due to its rich semantic content and associations among interactive entities, it can effectively alleviate inherent limitations in collaborative filtering (CF), such as data sparsity or cold-start issues. However, most existing knowledge-aware recommendation models indiscriminately aggregate all information in KG, without considering information specifically relevant to the recommendation task. Such indiscriminate aggregation could introduce additional noisy knowledge into representation learning, which can distort the understanding of users' genuine preferences, thereby sacrificing the recommendation quality. In this paper, we introduce the principle of invariance to the knowledge-aware recommendation, culminating in our Knowledge Graph Invariant Learning (KGIL) framework. It aims to discern and harness the task-relevant knowledge connections within KG to enhance the recommendation models. Specifically, we employ multiple environment generators to simulate diverse noisy KG-environments. Then we devise a novel attention learning mechanism for KG and user-item interaction graph, aiming to learn environment-invariant subgraphs. Leveraging an adversarial optimization strategy, we enhance the diversity of the environments, meanwhile, promote invariant representation learning across environments. We conduct extensive experiments on three datasets and compare KGIL with state-of-the-art methods. The experimental results further demonstrate the superiority of our approach.
Supplemental Material
MP4 File
Supplemental video
- Download
- 6.83 MB
References
[1]
Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish. 2021. Invariance principle meets information bottleneck for out-of-distribution generalization. NeurIPS, Vol. 34 (2021), 3438--3450.
[2]
Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018. Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms, Vol. 11, 9 (2018), 137.
[3]
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019).
[4]
Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017).
[5]
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. NeurIPS, Vol. 26 (2013).
[6]
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.
[7]
Shiyu Chang, Yang Zhang, Mo Yu, and Tommi Jaakkola. 2020. Invariant rationalization. In ICML. 1448--1458.
[8]
Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang. 2021. A unified lottery ticket hypothesis for graph neural networks. In ICML. PMLR, 1695--1706.
[9]
Xuxi Chen, Zhenyu Zhang, Yongduo Sui, and Tianlong Chen. 2020. GANs Can Play Lottery Tickets Too. In ICLR.
[10]
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel. 2021. Environment inference for invariant learning. In ICML. 2189--2200.
[11]
Xiaoyu Du, Zike Wu, Fuli Feng, Xiangnan He, and Jinhui Tang. 2022a. Invariant Representation Learning for Multimedia Recommendation. In Proceedings of the 30th ACM International Conference on Multimedia. 619--628.
[12]
Yuntao Du, Xinjun Zhu, Lu Chen, Ziquan Fang, and Yunjun Gao. 2022b. Metakg: Meta-learning on knowledge graph for cold-start recommendation. IEEE TKDE (2022).
[13]
Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, and Jian Tang. 2022. Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure. In NeurIPS.
[14]
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence, Vol. 2, 11 (2020), 665--673.
[15]
Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. 2020. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering, Vol. 34, 8 (2020), 3549--3568.
[16]
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.
[17]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173--182.
[18]
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 SIGKDD. 1531--1540.
[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 SIGKDD. 75--84.
[20]
Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. 2022b. Out-of-distribution generalization on graphs: A survey. arXiv preprint arXiv:2202.07987 (2022).
[21]
Haoyang Li, Ziwei Zhang, Xin Wang, and Wenwu Zhu. 2022c. Learning invariant graph representations for out-of-distribution generalization. In NeurIPS.
[22]
Yicong Li, Xiang Wang, Junbin Xiao, Wei Ji, and Tat-Seng Chua. 2022a. Invariant grounding for video question answering. In CVPR. 2928--2937.
[23]
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In WWW. 689--698.
[24]
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In AAAI, Vol. 29.
[25]
Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, and Meng Jiang. 2022. Graph Rationalization with Environment-based Augmentations. In SIGKDD. 1069--1078.
[26]
Yang Liu, Xiang Ao, Fuli Feng, Yunshan Ma, Kuan Li, Tat-Seng Chua, and Qing He. 2023. FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on Graphs. In SIGKDD. 1548--1558.
[27]
Ye Liu, Yao Wan, Lifang He, Hao Peng, and S Yu Philip. 2021. Kg-bart: Knowledge graph-augmented bart for generative commonsense reasoning. In AAAI, Vol. 35. 6418--6425.
[28]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
[29]
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters. 2018. Invariant models for causal transfer learning. The Journal of Machine Learning Research, Vol. 19, 1 (2018), 1309--1342.
[30]
Elan Rosenfeld, Pradeep Kumar Ravikumar, and Andrej Risteski. 2020. The Risks of Invariant Risk Minimization. In ICLR.
[31]
Yongduo Sui, Tianlong Chen, Pengfei Xia, Shuyao Wang, and Bin Li. 2022a. Towards robust detection and segmentation using vertical and horizontal adversarial training. In IJCNN. IEEE, 1--8.
[32]
Yongduo Sui, Wenyu Mao, Shuyao Wang, Xiang Wang, Jiancan Wu, Xiangnan He, and Tat-Seng Chua. 2024. Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning. ACM Transactions on Knowledge Discovery from Data (2024).
[33]
Yongduo Sui, Xiang Wang, Tianlong Chen, Meng Wang, Xiangnan He, and Tat-Seng Chua. 2023 a. Inductive Lottery Ticket Learning for Graph Neural Networks. Journal of Computer Science and Technology (2023).
[34]
Yongduo Sui, Xiang Wang, Jiancan Wu, Xiangnan He, and Tat-Seng Chua. 2022b. Causal Attention for Interpretable and Generalizable Graph Classification. In SIGKDD. 1696--1705.
[35]
Yongduo Sui, Xiang Wang, Jiancan Wu, An Zhang, and Xiangnan He. 2022c. Adversarial Causal Augmentation for Graph Covariate Shift. arXiv preprint arXiv:2211.02843 (2022).
[36]
Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, and Xiangnan He. 2023 b. Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift. In NeurIPS. https://openreview.net/pdf?id=hIGZujtOQv
[37]
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019).
[38]
Yu Tian, Yuhao Yang, Xudong Ren, Pengfei Wang, Fangzhao Wu, Qian Wang, and Chenliang Li. 2021. Joint knowledge pruning and recurrent graph convolution for news recommendation. In SIGIR. 51--60.
[39]
Riku Togashi, Mayu Otani, and Shin'ichi Satoh. 2021. Alleviating cold-start problems in recommendation through pseudo-labelling over knowledge graph. In WSDM. 931--939.
[40]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. 2017. Graph attention networks. stat, Vol. 1050, 20 (2017), 10--48550.
[41]
Chao Wang, Hengshu Zhu, Qiming Hao, Keli Xiao, and Hui Xiong. 2021c. Variable interval time sequence modeling for career trajectory prediction: Deep collaborative perspective. In Proceedings of the Web Conference 2021. 612--623.
[42]
Chao Wang, Hengshu Zhu, Peng Wang, Chen Zhu, Xi Zhang, Enhong Chen, and Hui Xiong. 2021d. Personalized and explainable employee training course recommendations: A bayesian variational approach. ACM Transactions on Information Systems (TOIS), Vol. 40, 4 (2021), 1--32.
[43]
Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Enhong Chen, and Hui Xiong. 2023. SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender System. ACM Transactions on Information Systems, Vol. 42, 2 (2023), 1--32.
[44]
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.
[45]
Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018b. DKN: Deep knowledge-aware network for news recommendation. In WWW. 1835--1844.
[46]
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 SIGKDD. 968--977.
[47]
Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019 e. Knowledge graph convolutional networks for recommender systems. In WWW. 3307--3313.
[48]
Shuyao Wang, Yongduo Sui, Jiancan Wu, Zhi Zheng, and Hui Xiong. 2024. Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation. In WSDM. ACM.
[49]
Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019a. Kgat: Knowledge graph attention network for recommendation. In SIGKDD. 950--958.
[50]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019b. Neural graph collaborative filtering. In SIGIR. 165--174.
[51]
Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, and Tat-Seng Chua. 2021a. Learning intents behind interactions with knowledge graph for recommendation. In WWW. 878--887.
[52]
Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. 2019c. Explainable reasoning over knowledge graphs for recommendation. In AAAI, Vol. 33. 5329--5336.
[53]
Yu Wang, Zhiwei Liu, Ziwei Fan, Lichao Sun, and Philip S Yu. 2021b. DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN. arXiv preprint arXiv:2108.11883 (2021).
[54]
Yanfang Wang, Yongduo Sui, Xiang Wang, Zhenguang Liu, and Xiangnan He. 2022b. Exploring lottery ticket hypothesis in media recommender systems. International Journal of Intelligent Systems, Vol. 37, 5 (2022), 3006--3024.
[55]
Zimu Wang, Yue He, Jiashuo Liu, Wenchao Zou, Philip S Yu, and Peng Cui. 2022a. Invariant preference learning for general debiasing in recommendation. In SIGKDD. 1969--1978.
[56]
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.
[57]
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.
[58]
Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, and Xiangnan He. 2023 a. GIF: A General Graph Unlearning Strategy via Influence Function. In WWW. 651--661.
[59]
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, et al. 2023 b. A Survey on Large Language Models for Recommendation. arXiv preprint arXiv:2305.19860 (2023).
[60]
Qitian Wu, Hengrui Zhang, Junchi Yan, and David Wipf. 2022b. Handling Distribution Shifts on Graphs: An Invariance Perspective. In ICLR.
[61]
Yingxin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua. 2022a. Discovering Invariant Rationales for Graph Neural Networks. In ICLR.
[62]
Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose. 2019. Relational collaborative filtering: Modeling multiple item relations for recommendation. In SIGIR. 125--134.
[63]
Yuhao Yang, Chao Huang, Lianghao Xia, and Chunzhen Huang. 2023. Knowledge Graph Self-Supervised Rationalization for Recommendation. In SIGKDD. 3046--3056.
[64]
Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022. Knowledge graph contrastive learning for recommendation. In SIGIR. 1434--1443.
[65]
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. NeurIPS, Vol. 33 (2020), 5812--5823.
[66]
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 Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. 1294--1303.
[67]
An Zhang, Jingnan Zheng, Xiang Wang, Yancheng Yuan, and Tat-Seng Chua. 2023 b. Invariant Collaborative Filtering to Popularity Distribution Shift. In WWW 2023. 1240--1251.
[68]
Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In SIGKDD. 353--362.
[69]
Yang Zhang, Tianhao Shi, Fuli Feng, Wenjie Wang, Dingxian Wang, Xiangnan He, and Yongdong Zhang. 2023 a. Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation. arXiv preprint arXiv:2304.13643 (2023).
[70]
Zhi Zheng, Zhaopeng Qiu, Xiao Hu, Likang Wu, Hengshu Zhu, and Hui Xiong. 2023 a. Generative job recommendations with large language model. arXiv preprint arXiv:2307.02157 (2023).
[71]
Zhi Zheng, Zhaopeng Qiu, Hui Xiong, Xian Wu, Tong Xu, Enhong Chen, and Xiangyu Zhao. 2022a. DDR: Dialogue Based Doctor Recommendation for Online Medical Service. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4592--4600.
[72]
Zhi Zheng, Zhaopeng Qiu, Tong Xu, Xian Wu, Xiangyu Zhao, Enhong Chen, and Hui Xiong. 2022b. CBR: context bias aware recommendation for debiasing user modeling and click prediction. In Proceedings of the ACM Web Conference 2022. 2268--2276.
[73]
Zhi Zheng, Ying Sun, Xin Song, Hengshu Zhu, and Hui Xiong. 2023 b. Generative Learning Plan Recommendation for Employees: A Performance-aware Reinforcement Learning Approach. In Proceedings of the 17th ACM Conference on Recommender Systems. 443--454.
[74]
Zhi Zheng, Chao Wang, Tong Xu, Dazhong Shen, Penggang Qin, Baoxing Huai, Tongzhu Liu, and Enhong Chen. 2021. Drug package recommendation via interaction-aware graph induction. In Proceedings of the Web Conference 2021. 1284--1295.
[75]
Zhi Zheng, Chao Wang, Tong Xu, Dazhong Shen, Penggang Qin, Xiangyu Zhao, Baoxing Huai, Xian Wu, and Enhong Chen. 2023 c. Interaction-aware drug package recommendation via policy gradient. ACM Transactions on Information Systems, Vol. 41, 1 (2023), 1--32.
[76]
Xinjun Zhu, Yuntao Du, Yuren Mao, Lu Chen, Yujia Hu, and Yunjun Gao. 2023. Knowledge-refined Denoising Network for Robust Recommendation. In SIGIR.
[77]
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. io
Index Terms
- Unleashing the Power of Knowledge Graph for Recommendation via Invariant Learning
Recommendations
Effective Knowledge-Aware Recommendation via Graph Convolutional Networks
Web Information Systems and ApplicationsAbstractMost existing graph neural network (GNN)-based knowledge-aware recommendation models rely on handcrafted feature engineering and do not allow for end-to-end training. As a state-of-the-art end-to-end framework, the Knowledge-aware Graph Neural ...
Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data MiningSolving cold-start problems is indispensable to provide meaningful recommendation results for new users and items. Under sparsely observed data, unobserved user-item pairs are also a vital source for distilling latent users' information needs. Most ...
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalMultimedia-based recommendation provides personalized item suggestions by learning the content preferences of users. With the proliferation of digital devices and APPs, a huge number of new items are created rapidly over time. How to quickly provide ...
Comments
Information & Contributors
Information
Published In
May 2024
4826 pages
ISBN:9798400701719
DOI:10.1145/3589334
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Proceedings Chair:
- Roy Ka-Wei Lee,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw
Copyright © 2024 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
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Project funded by China Postdoctoral Science Foundation
- National Natural Science Foundation of China
- Science and Technology Planning Project of Guangdong Province
- the Foshan HKUST Projects
Conference
WWW '24
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 382Total Downloads
- Downloads (Last 12 months)382
- Downloads (Last 6 weeks)74
Reflects downloads up to 22 Dec 2024
Other Metrics
Citations
Cited By
View all- Tachioka Y(2024)User Knowledge Prompt for Sequential RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691714(1142-1146)Online publication date: 8-Oct-2024
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in