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 hashing, BACO is built on the idea of exploiting collaborative signals in user-item interactions for user and item groupings, such that similar users/items share the same embeddings in the codebook. Specifically, we formulate a balanced co-clustering objective that maximizes intra-cluster connectivity while enforcing cluster-volume balance, and unify canonical graph clustering techniques into the framework through rigorous theoretical analyses. To produce effective groupings while averting codebook collapse, BACO instantiates this framework with a principled weighting scheme for users and items, an efficient label propagation solver, as well as secondary user clusters. Our extensive experiments comparing BACO against full models and 18 baselines over benchmark datasets demonstrate that BACO cuts embedding parameters by over 75% with a drop of at most 1.85% in recall, while surpassing the strongest baselines by being up to 346X faster.

翻译：推荐系统在过去十年中取得了显著进步，通过深度学习模型将每个用户/物品转化为稠密嵌入向量。在工业级规模下，由所有用户/物品的此类向量构成的嵌入表需要海量参数，并在训练和推理过程中带来沉重的计算与内存开销，阻碍了资源受限场景下的模型部署。现有嵌入压缩方案要么严重损害推荐准确性，要么产生高昂计算成本。为缓解这些问题，本文提出BACO——一种快速有效的嵌入表压缩框架。与传统的ID哈希不同，BACO基于利用用户-物品交互中的协同信号进行用户与物品分组的理念，使相似的用户/物品共享码本中的相同嵌入。具体而言，我们制定了平衡联合聚类目标，最大化簇内连接性的同时强制实现簇体积平衡，并通过严格的理论分析将经典图聚类技术统一纳入该框架。为在避免码本坍缩的同时产生有效的分组，BACO通过合理的用户与物品加权方案、高效的标签传播求解器以及辅助用户聚类来实例化该框架。我们将BACO与完整模型及基准数据集上的18个基线进行广泛对比实验，结果表明BACO可将嵌入参数削减超过75%，召回率至多下降1.85%，同时比最强基线快达346倍。