SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation

แนวคิดหลัก

The author proposes SSDRec to address over-denoising and under-denoising problems in sequential recommendation by augmenting sequences before denoising, guided by a global relation encoder. This three-stage framework enhances the reliability of sequence denoising and improves recommendations.

บทคัดย่อ

SSDRec introduces a novel approach to sequence denoising in sequential recommendation systems. By augmenting sequences with a self-augmentation module and employing a hierarchical denoising module, SSDRec outperforms state-of-the-art denoising methods across various datasets. The proposed method demonstrates flexibility and effectiveness in enhancing mainstream sequential recommendation models.
The content discusses the challenges of noise in user interaction sequences and presents SSDRec as a solution to improve the accuracy of recommendations. By inserting items before denoising, guided by inter-sequence relations, SSDRec shows superior performance over existing methods. The model's complexity analysis highlights its efficiency compared to baselines, making it a promising advancement in the field of recommendation systems.
Key metrics such as Hit Ratio (HR), Normalized Discounted Cumulative Gain (NDCG), and Mean Reciprocal Rank (MRR) are used to evaluate the performance of SSDRec against conventional sequential recommendation models and state-of-the-art denoising methods. Results show significant improvements across different datasets, confirming the effectiveness of SSDRec in enhancing sequential recommendation accuracy.

สถิติ

Over-denoising Ratio: 110.64%
Under-denoising Ratio: 50.90%

คำพูด

"The proposed SSDRec framework demonstrates flexible applicability and effectiveness across various mainstream sequential recommendation methods."
"SSDRec outperforms state-of-the-art denoising methods, showcasing its superiority in improving recommendations."

ข้อมูลเชิงลึกที่สำคัญจาก

SSDRec

by Chi Zhang,Qi... ที่ arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04278.pdf

สอบถามเพิ่มเติม

How does the self-augmentation approach of SSDRec impact the interpretability of recommendations

The self-augmentation approach of SSDRec impacts the interpretability of recommendations by providing a more reliable and noise-free sequence for the sequential recommendation models to learn from. By augmenting sequences with additional items selected based on global relations and inconsistencies detected in the target sequence, SSDRec enhances the quality of learned representations. This leads to more accurate recommendations that reflect user preferences without being influenced by noisy data. The interpretability is improved as the augmented sequences are cleaner and free from irrelevant or misleading information, allowing for better understanding of user behavior patterns and preferences.

What potential biases or limitations could arise from relying on global relation encoders for sequence augmentation

Relying on global relation encoders for sequence augmentation in SSDRec may introduce potential biases or limitations due to several factors:

Limited Scope: Global relation encoders may not capture all nuances and intricacies present in individual user-item interactions within a specific context. This could lead to oversimplification or generalization of relationships, potentially missing out on important details.

Data Quality: The effectiveness of global relation encoders heavily relies on the quality and relevance of input data used to construct multi-relation graphs. If there are biases or inaccuracies in this data, it can propagate through the learning process and impact the augmentation decisions.

Scalability Concerns: As datasets grow larger, constructing comprehensive multi-relation graphs becomes computationally intensive and challenging. This scalability issue can limit the encoder's ability to adapt effectively to diverse datasets with varying complexities.

Implicit Biases: The encoding process itself may introduce implicit biases based on how relations are defined or weighted within the graph structure, potentially leading to skewed interpretations during sequence augmentation.

Interpretation Challenges: Interpreting complex inter-sequence relationships encoded by global relation encoders might be challenging due to their abstract nature, making it harder to explain why certain items were selected for insertion into sequences.

To mitigate these biases and limitations, careful validation processes, robust evaluation techniques, transparency in model design choices, bias detection mechanisms during training/testing phases should be implemented alongside continuous monitoring for any unintended consequences arising from relying on global relation encoders.

How might incorporating user feedback mechanisms enhance the performance of SSDRec beyond traditional evaluation metrics

Incorporating user feedback mechanisms can enhance SSDRec's performance beyond traditional evaluation metrics by:

Personalization: User feedback allows for personalized recommendations tailored specifically towards individual preferences rather than generic trends observed across all users.

Dynamic Adaptation: Real-time feedback enables adaptive learning where recommendations evolve based on immediate changes in user behavior or interests.

3 .Bias Reduction: Direct feedback helps reduce algorithmic bias by incorporating explicit signals from users about their likes/dislikes which can counteract inherent biases present in historical interaction data.
4 .Enhanced Engagement: Feedback loops foster increased engagement as users feel heard when their inputs directly influence future recommendations.
5 .Improved Accuracy: Incorporating explicit feedback such as ratings or reviews provides richer information that can refine recommendation models leading to higher accuracy levels compared solely relying on implicit signals like clicks or purchases.
By integrating user feedback mechanisms into SSDRec's workflow effectively capturing direct signals from users regarding item preferences satisfaction levels will result in more relevant personalized recommendations ultimately enhancing overall system performance beyond conventional metrics like HR@K NDCG@K MRR@K