  Recently, deep learning models have been widely spread in the industrial
recommender systems and boosted the recommendation quality. Though having
achieved remarkable success, the design of task-aware recommender systems
usually requires manual feature engineering and architecture engineering from
domain experts. To relieve those human efforts, we explore the potential of
neural architecture search (NAS) and introduce AMEIR for Automatic behavior
Modeling, interaction Exploration and multi-layer perceptron (MLP)
Investigation in the Recommender system. The core contributions of AMEIR are
the three-stage search space and the tailored three-step searching pipeline.
Specifically, AMEIR divides the complete recommendation models into three
stages of behavior modeling, interaction exploration, MLP aggregation, and
introduces a novel search space containing three tailored subspaces that cover
most of the existing methods and thus allow for searching better models. To
find the ideal architecture efficiently and effectively, AMEIR realizes the
one-shot random search in recommendation progressively on the three stages and
assembles the search results as the final outcome. Further analysis reveals
that AMEIR's search space could cover most of the representative recommendation
models, which demonstrates the universality of our design. The extensive
experiments over various scenarios reveal that AMEIR outperforms competitive
baselines of elaborate manual design and leading algorithmic complex NAS
methods with lower model complexity and comparable time cost, indicating
efficacy, efficiency and robustness of the proposed method.
