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This paper investigates the role of Deep Neural Networks (DNNs) in feature interaction models for recommendation systems, focusing on their ability to mitigate dimensional collapse of embeddings. Through comprehensive experiments with parallel and stacked DNN architectures, the authors demonstrate that DNNs enhance the dimensional robustness of feature representations. A gradient-based theoretical analysis, validated by empirical results, elucidates the mechanisms by which DNNs alleviate dimensional collapse.
DNNs in recommendation models don't just learn feature interactions, they fundamentally reshape embedding spaces by preventing dimensional collapse.
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.
