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

PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023(2023)

Univ Alberta

Cited 0|Views20
Abstract
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, these techniques typically select a fixed subset of features or embedding dimensions for all instances and feed all instances into one recommender model without considering heterogeneity between items or users. This paper proposes a novel instance-wise Hierarchical Architecture Search framework, iHAS, which automates neural architecture search at the instance level. Specifically, iHAS incorporates three stages: searching, clustering, and retraining. The searching stage identifies optimal instance-wise embedding dimensions across different field features via carefully designed Bernoulli gates with stochastic selection and regularizers. After obtaining these dimensions, the clustering stage divides samples into distinct groups via a deterministic selection approach of Bernoulli gates. The retraining stage then constructs different recommender models, each one designed with optimal dimensions for the corresponding group. We conduct extensive experiments to evaluate the proposed iHAS on two public benchmark datasets from a real-world recommender system. The experimental results demonstrate the effectiveness of iHAS and its outstanding transferability to widely-used deep recommendation models.
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recommender system,instance-wise,embedding dimension search
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要点】:本文提出了一种名为iHAS的实例级分层架构搜索框架,通过自动化神经架构搜索,为不同实例选择最优的嵌入维度,以解决推荐系统中过拟合和计算成本高等问题。

方法】:iHAS框架包括搜索、聚类和重训练三个阶段。搜索阶段通过精心设计的伯努利门控和正则化器,识别不同字段特征的最优实例级嵌入维度。聚类阶段通过伯努利门的确定性选择方法将样本分为不同组。重训练阶段为每个组构建具有最优维度的不同推荐模型。

实验】:作者在两个公开基准数据集上进行了广泛的实验,验证了iHAS的有效性及其在广泛使用的深度推荐模型中的迁移性。数据集名称未在摘要中明确提及。