Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation

Keskeiset käsitteet

Certain data augmentation strategies can achieve similar or superior performance compared to some contrastive learning-based methods, demonstrating the potential to alleviate the data sparsity issue with fewer computational overhead.

Tiivistelmä

The study compares data augmentation and contrastive learning in sequential recommendation systems. It explores the effectiveness of eight popular data augmentation strategies and three contrastive learning methods on four real-world datasets. The results show that certain data augmentation strategies can outperform contrastive learning methods, especially in cold-start scenarios and with less computational complexity. The study highlights the importance of reevaluating the role of data augmentation in improving sequential recommendation performance.

Tilastot

Recent research utilizes contrastive learning (CL) to alleviate data sparsity in Sequential Recommender Systems (SRS).
Eight widely used sequence-level data augmentation strategies are benchmarked.
Certain data augmentation strategies can achieve similar or superior performance compared to some CL-based methods.

Lainaukset

"Contrastive learning for recommendation has attracted increasing attention due to its remarkable capability to enhance item representations through self-supervised signals."
"In most cases, less training data available leads to more significant relative performance improvement by different augmentation strategies."
"Excessive random augmentations introduce too much noise during model training, affecting the model's ability to capture user interests."

Tärkeimmät oivallukset

Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation

by Peilin Zhou,... klo arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11136.pdf

Syvällisempiä Kysymyksiä

How does the integration of slide-window strategy impact the performance of different sequence-level augmentation techniques

The integration of the slide-window strategy has a significant impact on the performance of different sequence-level augmentation techniques. In the study, it was observed that when combined with other augmentation strategies or contrastive learning methods, the slide-window strategy led to improved recommendation performance. Specifically, on datasets with shorter average sequence lengths like Beauty and Sports, the slide-window strategy achieved substantial improvements in Recall@20 and NDCG@20. Additionally, when integrated with various data augmentation strategies or contrastive learning-based SR methods, the slide-window strategy further boosted recommendation performance.

What are the implications of using simple sequence-level augmentations over complex contrastive learning methods

Using simple sequence-level augmentations over complex contrastive learning methods can have several implications. The study showed that certain data augmentation strategies could achieve similar or even superior performance compared to some state-of-the-art contrastive learning-based SR methods. This suggests that direct data augmentation strategies may be more efficient in alleviating data sparsity issues in sequential recommender systems while requiring less computational overhead. The findings indicate that while contrastive learning is popular for addressing data sparsity issues, it may not always be necessary and simpler approaches can yield comparable results.

How can the findings from this study be applied to other domains beyond sequential recommendation systems

The findings from this study can be applied to other domains beyond sequential recommendation systems by highlighting the effectiveness of simple sequence-level augmentations in improving model performance and mitigating data sparsity issues. These insights can be valuable for researchers and practitioners working on various machine learning tasks where dealing with sparse data is a challenge. By understanding that certain basic data augmentation techniques can offer competitive results without the complexity of advanced algorithms like contrastive learning, developers in different domains can optimize their models efficiently and effectively tackle sparse datasets.