Feature Interaction Modeling

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Feature interaction modeling studies how the effect of one input depends on other inputs, allowing models to capture combinations that additive methods miss. It matters in settings such as recommendation, tabular prediction, forecasting, and scientific data analysis, where useful signals may arise from feature combinations rather than individual variables. Common approaches include multiplicative or tensor-based interactions, attention and token mixing, selective interaction layers, and interpretable decompositions using GAMs, ANOVA, or Shapley methods. A central tension is how to recover meaningful higher-order structure without introducing spurious interactions, excessive computation, or explanations that fail under correlated or limited data.

Latest papers

71 papers11 papers in the last four weeks, up 38% on the four weeks before. 0.1% of all new papers.

Constructing Composite Features for Interpretable Music-Tagging
Mar 30, 2026Chenhao Xue, Weitao Hu, Joyraj Chakraborty +5Music Information Retrieval Automated Feature Engineering
University of Oxford · Independent Researcher · University of Toronto +1
TabKD: Tabular Knowledge Distillation through Interaction Diversity of Learned Feature Bins
Mar 16, 2026Shovon Niverd Pereira, Krishna Khadka, Yu LeiFeature Interaction Modeling Data-Free Knowledge Distillation
Department of Computer Science and Engineering, The University of Texas at Arlington
Deferred is Better: A Framework for Multi-Granularity Deferred Interaction of Heterogeneous Features
Mar 13, 2026Yi Xu, Moyu Zhang, Chaofan Fan +3Feature Interaction Modeling CTR Prediction
Alibaba International Digital Commerce Group Beijing, China
Fine-grained Motion Retrieval via Joint-Angle Motion Images and Token-Patch Late Interaction
Mar 10, 2026Yao Zhang, Zhuchenyang Liu, Yanlan He +2Representation Learning Feature Interaction Modeling
Aalto University, Espoo, Finland · Fudan University, Shanghai, China · Georgia Institute of Technology, Atlanta GA, USA
Exact Functional ANOVA Decomposition for Categorical Inputs Models
Mar 3, 2026Baptiste Ferrere, Nicolas Bousquet, Fabrice Gamboa +2Feature Attribution Shapley Value Attribution
IMT, SINCLAIR AI Lab · EDF R&D, SINCLAIR Lab · Université de Toulouse +6
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
Feb 18, 2026Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli +3Feature Attribution Feature Interaction Modeling
Leibniz Institute for Prevention Research and Epidemiology – BIPS · Faculty of Mathematics and Computer Science, University of Bremen · University of Warsaw +3
Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects
Feb 18, 2026Vasilis Gkolemis, Loukas Kavouras, Dimitrios Kyriakopoulos +5Interpretable ML Feature Interaction Modeling
ATHENA RC, Greece · HUA, Greece · Max Planck ISS, Germany +1
tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions
Feb 16, 2026Alessio Mazzetto, Mohammad Mahdi Khalili, Laura Fee Nern +3Feature Interaction Modeling Tabular Prediction
Brown University · Department of Computer Science Brown University Providence, RI, United States · Ohio State University +9
URBAN-SPIN: A street-level bikeability index to inform design implementations in historical city centres
Jan 30, 2026Haining Ding, Chenxi Wang, Simon Ladouce +1Feature Interaction Modeling Urban Planning
Cambridge Cognitive Architecture, Department of Architecture, University of Cambridge, UK · NeuroCivitas Lab for NeuroArchitecture, Centre for Research in the Arts, Social Sciences and Humanities (CRASSH), University of Cambridge, UK
KITINet: KInetic Theory Inspired Inter-Channel Information Exchange for Residual Networks
May 23, 2025Mingquan Feng, Yifan Fu, Tongcheng Zhang +3Feature Interaction Modeling Residual Learning
Shanghai Jiao Tong University
Enhancing High-order Interaction Awareness in LLM-based Recommender Model
Sep 30, 2024Xinfeng Wang, Jin Cui, Fumiyo Fukumoto +1Sequential Recommendation Feature Interaction Modeling
Graduate School of Engineering University of Yamanashi, Kofu, Japan · Interdisciplinary Graduate School University of Yamanashi, Kofu, Japan

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Constructing Composite Features for Interpretable Music-Tagging

Chenhao Xue, Weitao Hu, Joyraj Chakraborty, Zhijin Guo+4

University of Oxford · Independent Researcher · University of Toronto+1

Music Information Retrieval Automated Feature Engineering Interpretable ML Feature Interaction Modeling

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Combining multiple audio features can improve the performance of music tagging, but common deep learning-based feature fusion methods often lack interpretability. To address this problem, we propose a Genetic Programming (GP) pipeline that automatically evolves composite features by mathematically combining base music features, thereby capturing synergistic interactions while preserving interpretability. This approach provides representational benefits similar to deep feature fusion without sacrificing interpretability. Experiments on the MTG-Jamendo and GTZAN datasets demonstrate consistent improvements compared to state-of-the-art systems across base feature sets at different abstraction levels. It should be noted that most of the performance gains are noticed within the first few hundred GP evaluations, indicating that effective feature combinations can be identified under modest search budgets. The top evolved expressions include linear, nonlinear, and conditional forms, with various low-complexity solutions at top performance aligned with parsimony pressure to prefer simpler expressions. Analyzing these composite features further reveals which interactions and transformations tend to be beneficial for tagging, offering insights that remain opaque in black-box deep models.