Hi-GNN: hierarchical interactive graph neural networks for auxiliary information-enhanced recommendation
Pages 115 - 145
Abstract
Networked auxiliary information (e.g., user social network, item transition network, etc.) plays a significant role to alleviate the sparse behavioral information (e.g., click, purchase, rating, etc.) in recent recommender systems, which promotes auxiliary information-enhanced recommendation (AIER) to be flourishing. However, existing studies on AIER-treated auxiliary information and behavioral information independently and ignored complex relationships between two types of information, which leads to suboptimal recommendation performance. Toward to this end, we propose hierarchical interactive graph neural networks, short for Hi-GNN, for AIER. Specifically, we firstly learn the behavioral information and the auxiliary information from user and item sides by recursively performing graph neural networks. And then, we design the hierarchical interaction layer to model the relative importance and the mutual association between the behavioral information and the auxiliary information, which furthermore improves performance of AIER by more rationally integrating networked auxiliary information. Experimental results on three real-world datasets demonstrate that Hi-GNN outperforms state-of-the-art methods on recommendation performance and has better resistance to sparse data.
References
[1]
Li X, Grahl J, and Hinz O How do recommender systems lead to consumer purchases? A causal mediation analysis of a field experiment Inf Syst Res 2021 33 2 620-637
[2]
Gan M and Ma Y Deepinteract: multi-view features interactive learning for sequential recommendation Expert Syst Appl 2022 204 117305
[3]
Yu T, Guo J, Li W, and Lu M A mixed heterogeneous factorization model for non-overlapping cross-domain recommendation Dec Support Syst 2021 151 113625
[4]
Yu T, Guo J, Li W, and Lu M A mixed heterogeneous factorization model for non-overlapping cross-domain recommendation Dec Support Syst 2021 151 113625
[5]
Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) Bpr: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618
[6]
He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173–182
[7]
Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 165–174
[8]
He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Lightgcn: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 639–648
[9]
Wang X, Jin H, Zhang A, He X, Xu T, Chua T-S (2020) Disentangled graph collaborative filtering. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 1001–1010
[10]
Wahab OA, Rjoub G, Bentahar J, and Cohen R Federated against the cold: a trust-based federated learning approach to counter the cold start problem in recommendation systems Inf Sci 2022 601 189-206
[11]
Wu L, Sun P, Fu Y, Hong R, Wang X, Wang M (2019) A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 235–244
[12]
Wu L, Li J, Sun P, Hong R, Ge Y, and Wang M Diffnet++: a neural influence and interest diffusion network for social recommendation IEEE Trans Knowl Data Eng 2020 34 10 4753-4766
[13]
Guo J, Zhou Y, Zhang P, Song B, and Chen C Trust-aware recommendation based on heterogeneous multi-relational graphs fusion Inf Fusion 2021 74 87-95
[14]
Wang H, Zhao M, Xie X, Li W, Guo M (2019) Knowledge graph convolutional networks for recommender systems. In: The world wide web conference, pp 3307–3313
[15]
Wang X, He X, Cao Y, Liu M, Chua T-S (2019) Kgat: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 950–958
[16]
Gan M and Kwon OC A knowledge-enhanced contextual bandit approach for personalized recommendation in dynamic domain Knowl-Based Syst 2022 251 109158
[17]
Wang Z, Wei W, Cong G, Li X-L, Mao X-L, Qiu, M (2020) Global context enhanced graph neural networks for session-based recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 169–178
[18]
Zhang X, Lin H, Xu B, Li C, Lin Y, Liu H, and Ma F Dynamic intent-aware iterative denoising network for session-based recommendation Inf Process Manag 2022 59 3 102936
[19]
Wang Z, Wang Z, Li X, Yu Z, Guo B, Chen L, and Zhou X Exploring multi-dimension user-item interactions with attentional knowledge graph neural networks for recommendation IEEE Trans Big Data 2022 9 1 212-226
[20]
Song Y, Ye H, Li M, and Cao F Deep multi-graph neural networks with attention fusion for recommendation Expert Syst Appl 2022 191 116240
[21]
Lee J and Lee J-N Understanding the product information inference process in electronic word-of-mouth: an objectivity-subjectivity dichotomy perspective Inf Manag 2009 46 5 302-311
[22]
Hussain S, Guangju W, Jafar RMS, Ilyas Z, Mustafa G, and Jianzhou Y Consumers’ online information adoption behavior: motives and antecedents of electronic word of mouth communications Comput Hum Behav 2018 80 22-32
[23]
Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, and Bengio Y Graph attention networks Stat 2017 1050 20
[24]
Chen T, Yin H, Nguyen QVH, Peng W-C, Li X, Zhou X (2020) Sequence-aware factorization machines for temporal predictive analytics. In: 2020 IEEE 36th international conference on data engineering (ICDE), pp 1405–1416. IEEE
[25]
Xiao Z, Yang L, Jiang W, Wei Y, Hu Y, Wang H (2020) Deep multi-interest network for click-through rate prediction. In: Proceedings of the 29th ACM international conference on information and knowledge management, pp 2265–2268
[26]
Ma Y and Gan M Deepassociate: a deep learning model exploring sequential influence and history-candidate association for sequence recommendation Expert Syst Appl 2021 185 115587
[27]
Gan M and Cui H Exploring user movie interest space: a deep learning based dynamic recommendation model Expert Syst Appl 2021 173 114695
[28]
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29
[29]
Si Y, Zhang F, and Liu W An adaptive point-of-interest recommendation method for location-based social networks based on user activity and spatial features Knowl-Based Syst 2019 163 267-282
[30]
Yin F, Ji M, Wang Y, Yao Z, Feng X, and Li S Enhanced graph recommendation with heterogeneous auxiliary information Complex Intell Syst 2022 8 3 2311-2324
[31]
Liu H, Zheng C, Li D, Zhang Z, Lin K, Shen X, Xiong NN, and Wang J Multi-perspective social recommendation method with graph representation learning Neurocomputing 2022 468 469-481
[32]
Li Z, Cui Z, Wu S, Zhang X, Wang L (2019) Fi-GNN: modeling feature interactions via graph neural networks for CTR prediction. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 539–548
[33]
Su Y, Zhang R, Erfani MS, Gan J (2021) Neural graph matching based collaborative filtering. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 849–858
[34]
Liu Y, Gu Y, Ding Z, Gao J, Guo Z, Bao Y, Yan W (2020) Decoupled graph convolution network for inferring substitutable and complementary items. In: Proceedings of the 29th ACM international conference on information and knowledge management, pp 2621–2628
[35]
Chen T, Wong RC-W (2021) An efficient and effective framework for session-based social recommendation. In: Proceedings of the 14th ACM international conference on web search and data mining, pp 400–408
[36]
Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS (2019) Heterogeneous graph attention network. In: The world wide web conference, pp 2022–2032
[37]
Chen C, Ma W, Zhang M, Wang Z, He X, Wang C, Liu Y, Ma S (2021) Graph heterogeneous multi-relational recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 3958–3966
[38]
Wu L, He X, Wang X, Zhang K, and Wang M A survey on accuracy-oriented neural recommendation: from collaborative filtering to information-rich recommendation IEEE Trans Knowl Data Eng 2022 35 5 4425-4445
[39]
Zhao P, Luo A, Liu Y, Zhuang F, Xu J, Li Z, Sheng VS, and Zhou X Where to go next: a spatio-temporal gated network for next poi recommendation IEEE Trans Knowl Data Eng 2020 34 5 2512-2524
[40]
Monti F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein MM (2017) Geometric deep learning on graphs and manifolds using mixture model CNNs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5115–5124
[41]
Zhou G, Mou N, Fan Y, Pi Q, Bian W, Zhou C, Zhu X, Gai K (2019) Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 5941–5948
[42]
Feng Y, Lv F, Shen W, Wang M, Sun F, Zhu Y, Yang K (2019) Deep session interest network for click-through rate prediction. arXiv:1905.06482
[43]
Lian D, Liu Q, Chen E (2020) Personalized ranking with importance sampling. In: Proceedings of the web conference 2020, pp 1093–1103
[44]
Guo G, Zhang J, Yorke-Smith N (2013) A novel Bayesian similarity measure for recommender systems. In: IJCAI, vol 13, pp 2619–2625
[45]
Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1082–1090
[46]
Zhou G, Zhu X, Song C, Fan Y, Zhu H, Ma X, Yan Y, Jin J, Li H, Gai K (2018) Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1059–1068
[47]
Fayyaz Z, Ebrahimian M, Nawara D, Ibrahim A, and Kashef R Recommendation systems: algorithms, challenges, metrics, and business opportunities Appl Sci 2020 10 21 7748
[48]
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics, pp 249–256. JMLR workshop and conference proceedings
[49]
Liu R, Wu T, and Mozafari B Adam with bandit sampling for deep learning Adv Neural Inf Process Syst 2020 33 5393-5404
Index Terms
- Hi-GNN: hierarchical interactive graph neural networks for auxiliary information-enhanced recommendation
Index terms have been assigned to the content through auto-classification.
Recommendations
Personalized Recommendation Algorithm Using User Demography Information
WKDD '09: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data MiningPersonalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. User demography ...
Exploiting Group Information for Personalized Recommendation with Graph Neural Networks
Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ ...
Course Recommendation Based on Graph Convolutional Neural Network
Advances and Trends in Artificial Intelligence. Theory and ApplicationsAbstractSelecting the right learning content according to learners’ learning abilities and interests is the first and most important factor in achieving good learning performance. Based on the similarity between the course rating data in the Collaborative ...
Comments
Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 16 August 2023
Accepted: 25 July 2023
Revision received: 06 June 2023
Received: 14 September 2022
Author Tags
Qualifiers
- Research-article
Funding Sources
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Nov 2024
Other Metrics
Citations
View Options
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.
Sign in