Introduction

Couplings and interactions broadly refer to “any relationships and connections (for instance, cooccurrence, neighborhood, dependency, linkage, correlation, or causality) between two or more aspects, such as object, object class, object property (variable), process, fact and state of affairs, or other types of entities or properties (such as learners and learned results) appearing or produced prior to, during and after a target process (such as a learning task)” (Definition 2.1 [1]). Couplings also refer to “both well-explored relationships such as cooccurrence, neighborhood, dependency, linkage, correlation, and causality, and poorly explored and rarely studied ones such as sophisticated cultural and religious connections and influence.” [1]

In complex systems, couplings reflect interactions between factors related to technical, business (domain-specific) and environmental (including socio-cultural and economic) aspects. There are diverse forms of couplings embedded in poor-structured and ill-structured data. Such couplings and interactions are ubiquitous, implicit and/or explicit, objective and/or subjective, heterogeneous and/or homogeneous, presenting complexities to existing learning systems in statistics, mathematics and computer sciences, such as typical dependency, association and correlation relationships. Modeling and learning couplings and interactions is thus fundamental yet challenging. Such couplings and interactions may be present in any types, granularities, directions, orders, and hierarchies, and evolve over time and system developments.

Couplings and interactions have been studied in mathematics and statistics (e.g., coupling method), physics, cybernetics and control systems, computer programming, and social science. In AI and data science, coupling and interaction learning is still an open area, it focuses on quantifying, characterizing, representing, analyzing and learning diverse coupling relationships and interactions in systems, behaviors, and data. Coupling and interaction learning [1,2] aims to learn hierarchical, heterogeneous, multi-dimensional, multi-modal, multi-domain, multi-aspect, explicit and implicit, tangible and intangible, well-to-ill-structured, static and dynamic, and objective and/or subjective couplings and interactions between objects, sources, and systems, such as virtual/cyber systems, physical systems, social systems, economic systems, human systems, or across them.

Figure 1 illustrates the horizontal, vertical, hierarchical, heterogeneous, cross-domain, and cross-modal couplings and interactions within and between users and items, between users and items, within ratings, and between users, items and ratings in non-IID recommender systems [3].

Figure 1. Omniscient couplings in recommendation and non-IID recommender systems [3]

 
Research Topics

The research topics include but are not limited to the following areas:

• Coupling and interaction representation: learning representations of diverse couplings, e.g., subjective couplings, value-to-object hierarchical couplings, hidden couplings and interactions, heterogeneous couplings and interactions;
• Coupling-based similarity learning: learning similarity based on diverse couplings in data, behavior, and systems;
• Interaction network modeling: modeling the interactions and couplings in interaction networks.
• Textual coupling learning: learning syntactic and semantic couplings between words, entities, concepts, sentences, paragraphs, and documents;
• Multimedia coupling learning: learning couplings between multimedia data;
• Multimodal coupling learning: learning couplings between modalities, views, sources, channels, and databases;
• Heterogeneous coupling learning: learning heterogenous types of couplings, e.g., diverse coupling functions and distributions;
• Cross-model coupling learning: learning the couplings between models, methods, and algorithms;
• Cross-process coupling learning: learning the couplings between procedures and within processes;
• Cross-distribution coupling learning: learning the couplings between distributions and processes;
• Cross-domain coupling learning: learning the couplings between domains, markets, and systems.
• Nonstationary coupling learning: learning the couplings between nonstationary processes, intervals, and distributions.
• Output coupling learning: learning the couplings between outputs.

The following diagram in Figure 2 shows diverse couplings and coupling learning opportunities [1].

Figure 2. Various aspects of couplings [1]

 
References

• Coupling concepts

  • [1] Longbing Cao. Coupling Learning of Complex Interactions, Journal of Information Processing and Management, 51(2): 167-186 (2015). BibTeX

  • [2] Special Issue on Learning complex couplings and interactions, with IEEE Intelligent Systems, Can Wang, Fosca Giannotti, Longbing Cao: Learning Complex Couplings and Interactions, 36(1):3-5, IEEE Intelligent Systems, 2021.

  • [3] Longbing Cao. Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting. Engineering, 2: 212-224, doi:10.1016/J.ENG.2016.02.013., 2016.

• Coupling-based similarity/metric learning

  • Chengzhang Zhu, Longbing Cao and Jianpin Yin. Unsupervised Heterogeneous Coupling Learning for Categorical Representation. IEEE Transaction on Pattern Recognition and Machine Intelligence, 2020. BibTeX

  • Songlei Jian, Guansong Pang, Longbing Cao, Kai Lu and Hang Gao. CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning. IEEE Transactions on Knowledge and Data Engineering, 31(5): 853-866 (2019).

  • Chengzhang Zhu, Longbing Cao, Qiang Liu, Jianpin Yin and Vipin Kumar. Heterogeneous Metric Learning of Categorical Data with Hierarchical Couplings. IEEE Transactions on Knowledge and Data Engineering, DOI: 10.1109/TKDE.2018.2791525, 2018. BibTeX

  • Songlei Jian, Longbing Cao, Kai Lu, Hang Gao. Unsupervised Coupled Metric Similarity for Non-IID Categorical Data. IEEE Transactions on Knowledge and Data Engineering, 2018. BibTeX

  • Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao. Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning. IJCAI2017. BibTeX

  • Can Wang, Dong, Xiangjun; Zhou, Fei; Longbing Cao, Chi, Chi-Hung. Coupled Attribute Similarity Learning on Categorical Data (extension of the CIKM2011 paper), IEEE Transactions on Neural Networks and Learning Systems, 26(4): 781-797 (2015). BibTeX

  • Can Wang, Longbing Cao, Minchun Wang, Jinjiu Li, Wei Wei, Yuming Ou. Coupled Nominal Similarity in Unsupervised Learning, CIKM 2011, 973-978. BibTeX

• Coupling learning for downstream tasks

  • Quangui Zhang, Longbing Cao, Chengzhang Zhu, Zhiqiang Li and Jinguang Sun. CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering, IJCAI2018 BibTeX

  • Yonggang Huang, Yuying Liu, Longbing Cao, Jun Zhang, I Pan. Exploring Feature Coupling and Model Coupling for Image Source Identification, IEEE Transactions on Information Forensics & Security, 2018. BibTeX

  • Can Wang, Zhong She, Longbing Cao. Coupled Clustering Ensemble: Incorporating Coupling Relationships Both between Base Clusterings and Objects, ICDE2013. BibTeX

  • Chunming Liu, Longbing Cao. A Coupled k-Nearest Neighbor Algorithm for Multi-label Classification, PAKDD2015, 176-187. BibTeX

  • Fangfang Li, Guandong Xu, Longbing Cao. Coupled Matrix Factorization within Non-IID Context, PAKDD2015, 707-719. BibTeX

  • Chunming Liu, Longbing Cao, Philip S Yu. A Hybrid Coupled k-Nearest Neighbor Algorithm on Imbalance Data, IJCNN 2014. BibTeX

  • Chunming Liu, Longbing Cao, Philip S Yu. Coupled Fuzzy k-Nearest Neighbors Classification of Imbalanced Non-IID Categorical Data, IJCNN 2014. BibTeX

• Learning outlier couplings

  • Guansong Pang, Hongzuo Xu, Longbing Cao and Wentao Zhao. Selective Value Coupling Learning for Detecting Outliers in High-Dimensional Categorical Data. CIKM2017. BibTeX

  • Guansong Pang, Longbing Cao, Ling Cheny and Huan Liu. Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection. IJCAI2017. BibTeX

  • Guansong Pang, Longbing Cao, Ling Chen. Outlier Detection in Complex Categorical Data by Modelling the Feature Value Couplings. IJCAI2016: 1902-1908. BibTeX

  • Guansong Pang, Longbing Cao, Ling Chen, Huan Liu. Unsupervised Feature Selection for Outlier Detection by Modelling Hierarchical Value-Feature Couplings. ICDM2016. BibTeX

• Document/textual/word coupling learning

  • Shufeng Hao, Chongyang Shi, Zhendong Niu, Longbing Cao. Concept Coupling Learning for Improving Concept Lattice-based Document Retrieval. Engineering Applications of Artificial Intelligence, Volume 69, 65-75, 2018. BibTeX

  • Qianqian Chen, Liang Hu, Jia Xu, Wei Liu, Longbing Cao. Document similarity analysis via involving both explicit and implicit semantic couplings. DSAA 2015: 1-10. BibTeX

  • Xiangfu Meng, Longbing Cao and Jingyu Shao. Semantic Approximate Keyword Query Based on Keyword and Query Coupling Relationship Analysis. CIKM 2014: 529-538. BibTeX

  • Xin Cheng, Duoqian Miao, Can Wang, Longbing Cao. Coupled Term-Term Relation Analysis for Document Clustering, IJCNN2013. BibTeX

• Coupled behavior analysis/Cross-market couplings

  • Wei Cao, Liang Hu, Longbing Cao. Deep Modeling Complex Couplings within Financial Markets, AAAI2015, 2518-2524. BibTeX

  • Wei Cao, Yves Demazeau, Longbing Cao, Weidong Zhu. Financial crisis and global market couplings. DSAA 2015: 1-10. BibTeX

  • Yin Song, Longbing Cao, et al. Coupled Behavior Analysis for Capturing Coupling Relationships in Group-based Market Manipulation, KDD 2012, 976-984. BibTeX

  • Yin Song and Longbing Cao. Graph-based Coupled Behavior Analysis: A Case Study on Detecting Collaborative Manipulations in Stock Markets, IJCNN 2012, 1-8. BibTeX

  • Longbing Cao, Yuming Ou, Philip S Yu. Coupled Behavior Analysis with Applications, IEEE Trans. on Knowledge and Data Engineering, 24(8): 1378-1392 (2012). BibTeX

  • Wei Cao, Longbing Cao, Yin Song. Coupled Market Behavior Based Financial Crisis Detection, IJCNN2013. BibTeX

  • Longbing Cao, Yuming Ou, Philip S YU, Gang Wei. Detecting Abnormal Coupled Sequences and Sequence Changes in Group-based Manipulative Trading Behaviors, KDD2010, 85-94. BibTeX

• Coupled recommendation

  • Quangui Zhang, Longbing Cao, Chengzhang Zhu, Zhiqiang Li and Jinguang Sun. CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering. IJCAI2018.

  • Longbing Cao. Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting. Engineering, 2: 212-224, doi:10.1016/J.ENG.2016.02.013., 2016.

  • Fangfang Li, Guandong Xu, Longbing Cao. Coupled Matrix Factorization within Non-IID Context, PAKDD2015, 707-719.

  • Fangfang Li, Guandong Xu, Longbing Cao: Coupled Item-Based Matrix Factorization. WISE (1) 2014: 1-14

  • Fangfang Li, Guandong Xu, Longbing Cao, Zhendong Niu. Coupled Group-based Matrix Factorization for Recommender System, WISE 2013.