PME: pruning-based multi-size embedding for recommender systems.

ABSTRACT: Embedding is widely used in recommendation models to learn feature representations. However, the traditional embedding technique that assigns a fixed size to all categorical features may be suboptimal due to the following reasons. In recommendation domain, the majority of categorical features' embeddings can be trained with less capacity without impacting model performance, thereby storing embeddings with equal length may incur unnecessary memory usage. Existing work that tries to allocate customized sizes for each feature usually either simply scales the embedding size with feature's popularity or formulates this size allocation problem as an architecture selection problem. Unfortunately, most of these methods either have large performance drop or incur significant extra time cost for sea

SUBMITTER: Liu Z 

PROVIDER: S-EPMC10311001 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature