Learnable Embedding Sizes for Recommender Systems (2101.07577v2)

Abstract: The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manner that assigns a uniform size to all features has two issues. First, the numerous features inevitably lead to a gigantic embedding table that causes a high memory usage cost. Second, it is likely to cause the over-fitting problem for those features that do not require too large representation capacity. Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost. In this paper, we proposed a novel approach, named PEP (short for Plug-in Embedding Pruning), to reduce the size of the embedding table while avoiding the drop of recommendation accuracy. PEP prunes embedding parameter where the pruning threshold(s) can be adaptively learned from data. Therefore we can automatically obtain a mixed-dimension embedding-scheme by pruning redundant parameters for each feature. PEP is a general framework that can plug in various base recommendation models. Extensive experiments demonstrate it can efficiently cut down embedding parameters and boost the base model's performance. Specifically, it achieves strong recommendation performance while reducing 97-99% parameters. As for the computation cost, PEP only brings an additional 20-30% time cost compared with base models. Codes are available at https://github.com/ssui-liu/learnable-embed-sizes-for-RecSys.

• The paper introduces Plug-in Embedding Pruning (PEP) to automatically adjust embedding sizes based on feature relevance.
• It reduces parameter usage by 97-99% with only a 20-30% time overhead, significantly mitigating memory and overfitting issues.
• PEP consistently outperforms uniform embedding models on benchmarks like MovieLens-1M, Criteo, and Avazu, demonstrating robust practical value.

Analysis of "Learnable Embedding Sizes for Recommender Systems"

The paper "Learnable Embedding Sizes for Recommender Systems" presents a novel framework, Plug-in Embedding Pruning (PEP), directed at optimizing the use of embeddings in deep learning recommender models. This work addresses the inefficiencies of uniform-sized embedding tables, aiming to improve model performance by reducing memory usage and mitigating overfitting, two longstanding challenges in recommender system architectures.

Key Contributions

The paper identifies two primary issues in traditional embedding methods: the exponential growth of embedding tables due to numerous features and the overfitting risk for features that do not necessitate high-dimensional embeddings. To this end, PEP introduces a pruning technique whereby embedding parameters are adaptively learned and reduced based on their relevance to the model's accuracy.

Novel Approach: PEP's key innovation is its ability to automatically prune embedding parameters in a way that reflects each feature's importance, resulting in a mixed-dimension embedding scheme. This strategy not only conserves memory but also maintains, and in some cases enhances, recommendation accuracy.

Performance Metrics: PEP demonstrated an exceptional ability to reduce parameter usage by 97-99%, while incurring only a minor additional time cost (20-30%) compared to base models. This presents a significant improvement over previous methods that either compromised performance or required extensive computational resources.

Experimental Validation

The paper reports extensive experimentation on three benchmark datasets: MovieLens-1M, Criteo, and Avazu. The results consistently show that PEP can outperform traditional uniform embedding models and other state-of-the-art methods. Specifically, PEP is shown to be adept at managing the trade-off between recommendation accuracy and parameter usage.

PEP's integration into three recommendation systems—FM, DeepFM, and AutoInt—further underscores its versatility. The method’s robustness is particularly evident in scenarios demanding rapid recommendations on platforms with high-speed constraints, such as YouTube.

Theoretical and Practical Implications

From a theoretical perspective, PEP advances the understanding of how embedding-dimensionality impacts model generalization capabilities. It also introduces the potential of adaptive embedding sizes to reformulate sparse feature representations in machine learning tasks.

Practically, the work paves the way for more resource-efficient deep learning models in industrial recommender systems, especially where memory resources are limited. The empirical evidence of PEP’s efficacy positions it as a feasible solution to both latency and storage challenges in large-scale recommendation systems.

Future Prospects

While PEP offers notable advancements, the future work could explore several enhancements. There is potential for investigating other learning algorithms that may further optimize threshold determination without additional computational cost. The relationship between embedding sparsity and data characteristics also presents an intriguing avenue for future research, potentially leading to further empirical insights.

In summary, this paper provides a comprehensive paper of embedding optimization for recommender systems, making significant strides in memory efficiency and performance accuracy. It stands as a compelling resource for researchers and practitioners seeking to improve recommendation frameworks with adaptive embedding dimensions.