  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.
