1 research outputs found
AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System
User behavior and feature interactions are crucial in deep learning-based
recommender systems. There has been a diverse set of behavior modeling and
interaction exploration methods in the literature. Nevertheless, the design of
task-aware recommender systems still requires feature engineering and
architecture engineering from domain experts. In this work, we introduce AMER,
namely Automatic behavior Modeling and interaction Exploration in Recommender
systems with Neural Architecture Search (NAS). The core contributions of AMER
include the three-stage search space and the tailored three-step searching
pipeline. In the first step, AMER searches for residual blocks that incorporate
commonly used operations in the block-wise search space of stage 1 to model
sequential patterns in user behavior. In the second step, it progressively
investigates useful low-order and high-order feature interactions in the
non-sequential interaction space of stage 2. Finally, an aggregation
multi-layer perceptron (MLP) with shortcut connection is selected from flexible
dimension settings of stage~3 to combine features extracted from the previous
steps. For efficient and effective NAS, AMER employs the one-shot random search
in all three steps. Further analysis reveals that AMER's search space could
cover most of the representative behavior extraction and interaction
investigation methods, which demonstrates the universality of our design. The
extensive experimental results over various scenarios reveal that AMER could
outperform competitive baselines with elaborate feature engineering and
architecture engineering, indicating both effectiveness and robustness of the
proposed method