# Retrieval & Interaction Machine for Tabular Data Prediction

arXiv: https://arxiv.org/abs/2108.05252 · HTML: https://mlsanity.com/paper/2108.05252/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2021.

## Built on by (most cited first, 21 of 21)

- [TransTab: Learning Transferable Tabular Transformers Across Tables](https://mlsanity.com/paper/2205.09328/) (2022)
- [ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation](https://mlsanity.com/paper/2308.11131/) (2023)
- [TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023](https://mlsanity.com/paper/2307.14338/) (2023)
- [Retrieval & Fine-Tuning for In-Context Tabular Models](https://mlsanity.com/paper/2406.05207/) (2024)
- [Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution](https://mlsanity.com/paper/2408.10548/) (2024)
- [Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation](https://mlsanity.com/paper/2408.03533/) (2024)
- [Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions](https://mlsanity.com/paper/2401.02143/) (2024)
- [An Automatic Graph Construction Framework based on Large Language Models for Recommendation](https://mlsanity.com/paper/2412.18241/) (2024)
- [Learning Enhanced Representations for Tabular Data via Neighborhood Propagation](https://mlsanity.com/paper/2206.06587/) (2022)
- [Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning](https://mlsanity.com/paper/2311.07343/) (2023)
- [Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints](https://mlsanity.com/paper/2604.01841/) (2026)
- [TabGSL: Graph Structure Learning for Tabular Data Prediction](https://mlsanity.com/paper/2305.15843/) (2023)
- [CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics](https://mlsanity.com/paper/2406.14015/) (2024)
- [DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation](https://mlsanity.com/paper/2406.00011/) (2024)
- [ROMO: Retrieval-enhanced Offline Model-based Optimization](https://mlsanity.com/paper/2310.07560/) (2023)
- [Automated Model Selection for Tabular Data](https://mlsanity.com/paper/2401.00961/) (2024)
- [Retrieval and Distill: A Temporal Data Shift-Free Paradigm for Online Recommendation System](https://mlsanity.com/paper/2404.15678/) (2024)
- [Retrieval-Oriented Knowledge for Click-Through Rate Prediction](https://mlsanity.com/paper/2404.18304/) (2024)
- [FINED: Feed Instance-Wise Information Need with Essential and Disentangled Parametric Knowledge from the Past](https://mlsanity.com/paper/2406.00012/) (2024)
- [Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation](https://mlsanity.com/paper/2501.13344/) (2025)
- [Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases](https://mlsanity.com/paper/2508.08327/) (2025)

## Abstract (arXiv)

Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc. Tabular data is structured into rows and columns, with each row as a data sample and each column as a feature attribute. Both the columns and rows of the tabular data carry useful patterns that could improve the model prediction performance. However, most existing models focus on the cross-column patterns yet overlook the cross-row patterns as they deal with single samples independently. In this work, we propose a general learning framework named Retrieval & Interaction Machine (RIM) that fully exploits both cross-row and cross-column patterns among tabular data. Specifically, RIM first leverages search engine techniques to efficiently retrieve useful rows of the table to assist the label prediction of the target row, then uses feature interaction networks to capture the cross-column patterns among the target row and the retrieved rows so as to make the final label prediction. We conduct extensive experiments on 11 datasets of three important tasks, i.e., CTR prediction (classification), top-n recommendation (ranking) and rating prediction (regression). Experimental results show that RIM achieves significant improvements over the state-of-the-art and various baselines, demonstrating the superiority and efficacy of RIM.