iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models

• Published in: arXiv.org
• Main Authors: Yu, Yakun, Shi-ang, Qi, Yang, Jiuding, Jiang, Liyao, Niu, Di
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 14.09.2023
• Subjects: Clustering; Embedding; Heterogeneity; Recommender systems; Searching

Current recommender systems employ large-sized embedding tables with uniform dimensions for all features, leading to overfitting, high computational cost, and suboptimal generalizing performance. Many techniques aim to solve this issue by feature selection or embedding dimension search. However, the...