Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems
Recommender systems have advanced markedly over the past decade by transforming each user/item into a dense embedding vector with deep learning models. At industrial scale, embedding tables constituted by such vectors of all users/items demand a vast amount of parameters and impose heavy compute and memory overhead during training and inference, hindering model deployment under resource constraints. Existing solutions towards embedding compression either suffer from severely compromised recommendation accuracy or incur considerable computational costs. To mitigate these issues, this paper presents BACO, a fast and effective framework for compressing embedding tables. Unlike traditional ID...
| Authors | Runhao Jiang, Renchi Yang, Donghao Wu |
|---|---|
| Primary category | cs.IR |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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