  Maximizing the user-item engagement based on vectorized embeddings is a
standard procedure of recent recommender models. Despite the superior
performance for item recommendations, these methods however implicitly
deprioritize the modeling of user-wise similarity in the embedding space;
consequently, identifying similar users is underperforming, and additional
processing schemes are usually required otherwise. To avoid thorough model
re-training, we propose WSFE, a model-agnostic and training-free representation
encoder, to be flexibly employed on the fly for effective user segmentation.
Underpinned by the optimal transport theory, the encoded representations from
WSFE present a matched user-wise similarity/distance measurement between the
realistic and embedding space. We incorporate WSFE into six state-of-the-art
recommender models and conduct extensive experiments on six real-world
datasets. The empirical analyses well demonstrate the superiority and
generality of WSFE to fuel multiple downstream tasks with diverse underlying
targets in recommendation.
