  Learning feature interaction is the critical backbone to building recommender
systems. In web-scale applications, learning feature interaction is extremely
challenging due to the sparse and large input feature space; meanwhile,
manually crafting effective feature interactions is infeasible because of the
exponential solution space. We propose to leverage a Transformer-based
architecture with attention layers to automatically capture feature
interactions. Transformer architectures have witnessed great success in many
domains, such as natural language processing and computer vision. However,
there has not been much adoption of Transformer architecture for feature
interaction modeling in industry. We aim at closing the gap. We identify two
key challenges for applying the vanilla Transformer architecture to web-scale
recommender systems: (1) Transformer architecture fails to capture the
heterogeneous feature interactions in the self-attention layer; (2) The serving
latency of Transformer architecture might be too high to be deployed in
web-scale recommender systems. We first propose a heterogeneous self-attention
layer, which is a simple yet effective modification to the self-attention layer
in Transformer, to take into account the heterogeneity of feature interactions.
We then introduce \textsc{Hiformer} (\textbf{H}eterogeneous
\textbf{I}nteraction Trans\textbf{former}) to further improve the model
expressiveness. With low-rank approximation and model pruning, \hiformer enjoys
fast inference for online deployment. Extensive offline experiment results
corroborates the effectiveness and efficiency of the \textsc{Hiformer} model.
We have successfully deployed the \textsc{Hiformer} model to a real world large
scale App ranking model at Google Play, with significant improvement in key
engagement metrics (up to +2.66\%).
