  In collaborative filtering (CF), interaction function (IFC) plays the
important role of capturing interactions among items and users. The most
popular IFC is the inner product, which has been successfully used in low-rank
matrix factorization. However, interactions in real-world applications can be
highly complex. Thus, other operations (such as plus and concatenation), which
may potentially offer better performance, have been proposed. Nevertheless, it
is still hard for existing IFCs to have consistently good performance across
different application scenarios. Motivated by the recent success of automated
machine learning (AutoML), we propose in this paper the search for simple
neural interaction functions (SIF) in CF. By examining and generalizing
existing CF approaches, an expressive SIF search space is designed and
represented as a structured multi-layer perceptron. We propose an one-shot
search algorithm that simultaneously updates both the architecture and learning
parameters. Experimental results demonstrate that the proposed method can be
much more efficient than popular AutoML approaches, can obtain much better
prediction performance than state-of-the-art CF approaches, and can discover
distinct IFCs for different data sets and tasks
