Detecting Beneficial Feature Interactions for Recommender Systems
Open accessAbstract
Feature interactions are essential for achieving high accuracy in recommender systems. Many studies take into account the interaction between every pair of features. However, this is suboptimal because some feature interactions may not be that relevant to the recommendation result and taking them into account may introduce noise and decrease recommendation accuracy. To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy. The automatic feature interaction detection is achieved via edge prediction with an L0 activation regularization. Our proposed model is proved to be effective through the information bottleneck principle and statistical interaction theory. Experimental results show that our model (i) outperforms existing baselines in terms of accuracy, and (ii) automatically identifies beneficial feature interactions.
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The search strategy shifts from discrete reinforcement learning to differentiable searching, which adaptively selects architectures within one-shot learning to circumvent computational burden, for more efficient convergence.
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From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [188]
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Su, Y., Jiang, W., Lin, F., Yang, C., Erfani, S., Gan, J., Zhao, Y., Li, R., & Zhang, R. (2025) · ACM Transactions on Information Systems · doi:10.1145/3722553 · Cited by 1 · see citing works
Self-citationLightFIG: simplifying and powering feature interactions via graph for recommendation
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DNN2LR: Automatic Feature Crossing for Credit ScoringPreprint
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FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular DataPreprint
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Approach to Research Feature Interactions
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Cross Feature Engineering for Anti-Fraud Task in Insurance
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HGT: Transformer Architecture for Arbitrary Hypergraphs Based on P-Laplacian
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- Authors
- Su, Y., Zhang, R., Erfani, S., & Xu, Z.
- Affiliations
- The University of Melbourne (AU)
- Tsinghua University (CN)
- Hebei University of Technology (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2021-05-18
- Volume
- 35
- Issue
- 5
- Pages
- 4357-4365
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W3174341621
- Cited by
- 76
- References
- 36
- Citations by year
- 2026: 1 · 2025: 15 · 2024: 14 · 2023: 18 · 2022: 15 · 2021: 11 · 2020: 2
- FWCI
- 12.00
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- China Scholarship Council
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- feature interaction, recommender systems, graph neural networks, edge prediction, L0 regularization, information bottleneck theory, recommendation accuracy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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