# FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data

arXiv: https://arxiv.org/abs/2007.14573 · HTML: https://mlsanity.com/paper/2007.14573/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 13 of 13)

- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions](https://mlsanity.com/paper/2401.02143/) (2024)
- [DIWIFT: Discovering Instance-wise Influential Features for Tabular Data](https://mlsanity.com/paper/2207.02773/) (2022)
- [A Comprehensive Survey on Automated Machine Learning for Recommendations](https://mlsanity.com/paper/2204.01390/) (2022)
- [ColdNAS: Search to Modulate for User Cold-Start Recommendation](https://mlsanity.com/paper/2306.03387/) (2023)
- [pTNAS: Progressive Neural Architecture Search for Tabular Data](https://mlsanity.com/paper/2403.10318/) (2024)
- [AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine](https://mlsanity.com/paper/2311.14304/) (2023)
- [Global Layers: Non-IID Tabular Federated Learning](https://mlsanity.com/paper/2305.19290/) (2023)
- [Graph Neural Machine: A New Model for Learning with Tabular Data](https://mlsanity.com/paper/2402.02862/) (2024)
- [Cognitive Evolutionary Learning to Select Feature Interactions for Recommender Systems](https://mlsanity.com/paper/2405.18708/) (2024)
- [SCOPE-FE: Structured Control of Operator and Pairwise Exploration for Feature Engineering via Quality-Aware Candidate-Space Reduction](https://mlsanity.com/paper/2604.27025/) (2026)

## Abstract (arXiv)

High-order interactive features capture the correlation between different columns and thus are promising to enhance various learning tasks on ubiquitous tabular data. To automate the generation of interactive features, existing works either explicitly traverse the feature space or implicitly express the interactions via intermediate activations of some designed models. These two kinds of methods show that there is essentially a trade-off between feature interpretability and search efficiency. To possess both of their merits, we propose a novel method named Feature Interaction Via Edge Search (FIVES), which formulates the task of interactive feature generation as searching for edges on the defined feature graph. Specifically, we first present our theoretical evidence that motivates us to search for useful interactive features with increasing order. Then we instantiate this search strategy by optimizing both a dedicated graph neural network (GNN) and the adjacency tensor associated with the defined feature graph. In this way, the proposed FIVES method simplifies the time-consuming traversal as a typical training course of GNN and enables explicit feature generation according to the learned adjacency tensor. Experimental results on both benchmark and real-world datasets show the advantages of FIVES over several state-of-the-art methods. Moreover, the interactive features identified by FIVES are deployed on the recommender system of Taobao, a worldwide leading e-commerce platform. Results of an online A/B testing further verify the effectiveness of the proposed method FIVES, and we further provide FIVES as AI utilities for the customers of Alibaba Cloud.