Feature interactions can play a crucial role in recommendation systems as they capture complex relationships between user preferences and item characteristics. Existing methods such as Deep & Cross Network (DCNv2) may suffer from high computational requirements due to their cross-layer operations. In this paper, we propose a novel approach called blockwise feature interaction (BFI) to help alleviate this issue. By partitioning the feature interaction process into smaller blocks, we can significantly reduce both the memory footprint and the computational burden. Four variants (denoted by P, Q, T, S, respectively) of BFI have been developed and empirically compared. Our experimental results demonstrate that the proposed algorithms achieves close accuracy compared to the standard DCNv2, while greatly reducing the computational overhead and the number of parameters. This paper contributes to the development of efficient recommendation systems by providing a practical solution for improving feature interaction efficiency.

翻译：特征交互在推荐系统中至关重要，因为它能够捕捉用户偏好与物品特征之间的复杂关系。现有的方法如Deep & Cross Network（DCNv2）由于其跨层操作，可能面临计算开销大的问题。本文提出了一种称为分块特征交互（BFI）的新方法，以缓解这一问题。通过将特征交互过程划分为更小的块，我们能够显著降低内存占用和计算负担。我们开发了BFI的四种变体（分别记为P、Q、T、S），并进行了实证比较。实验结果表明，所提出的算法在接近标准DCNv2精度的同时，大幅减少了计算开销和参数数量。本文通过提供一种提高特征交互效率的实用方案，为高效推荐系统的发展做出了贡献。