  The embedding-based architecture has become the dominant approach in modern
recommender systems, mapping users and items into a compact vector space. It
then employs predefined similarity metrics, such as the inner product, to
calculate similarity scores between user and item embeddings, thereby guiding
the recommendation of items that align closely with a user's preferences. Given
the critical role of similarity metrics in recommender systems, existing
methods mainly employ handcrafted similarity metrics to capture the complex
characteristics of user-item interactions. Yet, handcrafted metrics may not
fully capture the diverse range of similarity patterns that can significantly
vary across different domains.
  To address this issue, we propose an Automated Similarity Metric Generation
method for recommendations, named AutoSMG, which can generate tailored
similarity metrics for various domains and datasets. Specifically, we first
construct a similarity metric space by sampling from a set of basic embedding
operators, which are then integrated into computational graphs to represent
metrics. We employ an evolutionary algorithm to search for the optimal metrics
within this metric space iteratively. To improve search efficiency, we utilize
an early stopping strategy and a surrogate model to approximate the performance
of candidate metrics instead of fully training models. Notably, our proposed
method is model-agnostic, which can seamlessly plugin into different
recommendation model architectures. The proposed method is validated on three
public recommendation datasets across various domains in the Top-K
recommendation task, and experimental results demonstrate that AutoSMG
outperforms both commonly used handcrafted metrics and those generated by other
search strategies.
