  Click-through rate prediction is one of the core tasks in commercial
recommender systems. It aims to predict the probability of a user clicking a
particular item given user and item features. As feature interactions bring in
non-linearity, they are widely adopted to improve the performance of CTR
prediction models. Therefore, effectively modelling feature interactions has
attracted much attention in both the research and industry field. The current
approaches can generally be categorized into three classes: (1) na\"ive
methods, which do not model feature interactions and only use original
features; (2) memorized methods, which memorize feature interactions by
explicitly viewing them as new features and assigning trainable embeddings; (3)
factorized methods, which learn latent vectors for original features and
implicitly model feature interactions through factorization functions. Studies
have shown that modelling feature interactions by one of these methods alone
are suboptimal due to the unique characteristics of different feature
interactions. To address this issue, we first propose a general framework
called OptInter which finds the most suitable modelling method for each feature
interaction. Different state-of-the-art deep CTR models can be viewed as
instances of OptInter. To realize the functionality of OptInter, we also
introduce a learning algorithm that automatically searches for the optimal
modelling method. We conduct extensive experiments on four large datasets. Our
experiments show that OptInter improves the best performed state-of-the-art
baseline deep CTR models by up to 2.21%. Compared to the memorized method,
which also outperforms baselines, we reduce up to 91% parameters. In addition,
we conduct several ablation studies to investigate the influence of different
components of OptInter. Finally, we provide interpretable discussions on the
results of OptInter.
