Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective

2026-04-29

AI Summary

This work addresses the ongoing debate regarding the ability of deep neural networks (DNNs) to effectively model high-order feature interactions in recommender systems. By investigating the phenomenon of dimensional collapse in embedding representations, we uncover— for the first time—the core mechanism through which DNNs enhance model expressiveness by mitigating such collapse. Through a combination of gradient-based theoretical analysis, ablation studies, and robustness evaluation across embedding dimensions, we systematically compare parallel and stacked DNN architectures. Our findings demonstrate that both structural variants significantly suppress dimensional collapse, thereby improving the modeling of feature interactions and ultimately boosting recommendation performance.

Abstract

DNNs have gained widespread adoption in feature interaction recommendation models. However, there has been a longstanding debate on their roles. On one hand, some works claim that DNNs possess the ability to implicitly capture high-order feature interactions. Conversely, recent studies have highlighted the limitations of DNNs in effectively learning dot products, specifically second-order interactions, let alone higher-order interactions. In this paper, we present a novel perspective to understand the effectiveness of DNNs: their impact on the dimensional robustness of the representations. In particular, we conduct extensive experiments involving both parallel DNNs and stacked DNNs. Our evaluation encompasses an overall study of complete DNN on two feature interaction models, alongside a fine-grained ablation analysis of components within DNNs. Experimental results demonstrate that both parallel and stacked DNNs can effectively mitigate the dimensional collapse of embeddings. Furthermore, a gradient-based theoretical analysis, supported by empirical evidence, uncovers the underlying mechanisms of dimensional collapse.

Problem

• DNNs
• feature interaction
• dimensional collapse
• recommendation models
• high-order interactions

Innovation

• dimensional collapse
• feature interaction
• deep neural networks
• embedding robustness
• gradient analysis