DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback

Yiqing Wu, Ruobing Xie, Zhao Zhang, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Zhanhui Kang, Yongjun Xu·May 24, 2024

Summary

The paper investigates the underutilization of negative feedback in graph-based recommendation systems, where existing models often rely on positive interactions. It reveals that current graph neural networks struggle to model negative feedback and suffer from representation degeneration, impacting recommendation quality. To address these issues, the authors propose Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN), which incorporates a dual-frequency graph filter to capture both positive and negative signals and a signed graph regularization to maintain consistent user/item embeddings. DFGNN outperforms baseline models in handling negative feedback and improving recommendation tasks, as demonstrated by experiments on real-world datasets. The study highlights the importance of considering both positive and negative feedback in recommendation systems and the effectiveness of DFGNN in capturing complex interactions.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper addresses two main problems in graph-based recommendation systems:

  1. Negative Feedback Modeling: The paper aims to tackle the challenge of effectively modeling negative feedback in graph-based recommendation systems. Negative feedback, such as user dislikes or low ratings, is crucial for improving recommendation accuracy and user satisfaction . This problem is not entirely new, as existing graph neural networks (GNNs) have struggled to adequately capture negative feedback, which acts as a high-frequency signal in user-item graphs .
  2. Representation Degeneration: The paper also focuses on the representation degeneration problem in graph-based recommendations. This issue arises due to the over-smoothing effect in GNNs, where node features become overly similar during training, leading to a loss of expressive power in the embeddings . While this problem has been identified in previous studies , the paper proposes a novel solution to alleviate representation degeneration by introducing a signed-graph regularization loss in the Dual-frequency Graph Neural Network (DFGNN) model .

In summary, the paper aims to address the challenges of negative feedback modeling and representation degeneration in graph-based recommendation systems, providing innovative solutions to enhance the performance and effectiveness of these systems.


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis that existing graph neural networks are not well-suited for modeling negative feedback, which acts as a high-frequency signal in a user-item graph, and that the graph-based recommendation suffers from the representation degeneration problem . The study aims to address the challenge of incorporating negative feedback, such as dislikes and low ratings, into graph-based recommendation systems, which has been underexplored in current research .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback" introduces several novel ideas, methods, and models in the field of graph-based recommendation systems . Here are the key contributions of the paper:

  1. Dual-frequency Graph Neural Network (DFGNN): The paper proposes the DFGNN model, which addresses the challenge of modeling both positive and negative feedback in graph-based recommendation systems. DFGNN utilizes a dual-frequency graph filter (DGF) to capture low-frequency and high-frequency signals representing positive and negative feedback, respectively. This approach allows for a more comprehensive analysis of user-item interactions .

  2. Signed Graph Regularization: To maintain the uniformity of user/item embeddings in the embedding space and alleviate representation degeneration issues, the paper introduces signed graph regularization. This regularization technique helps in improving the overall performance of the recommendation model by considering both positive and negative feedback signals .

  3. Analysis of Negative and Positive Feedback: The paper conducts an in-depth analysis of negative and positive feedback from a graph signal frequency perspective. This analysis forms the basis for the development of the DFGNN model and highlights the importance of considering both types of feedback for more effective recommendation systems .

  4. Experimental Validation: Extensive experiments are conducted on real-world datasets to validate the effectiveness of the proposed DFGNN model. The results demonstrate the power of DFGNN in capturing and utilizing both positive and negative feedback signals for improved recommendations .

Overall, the paper introduces a novel approach to graph-based recommendation systems by proposing the DFGNN model, incorporating dual-frequency graph filters and signed graph regularization to address the challenges associated with modeling negative feedback and representation degeneration in recommendation scenarios. The "DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback" paper introduces several key characteristics and advantages compared to previous methods in the field of graph-based recommendation systems .

  1. Modeling Negative Feedback: One of the main characteristics of DFGNN is its ability to effectively model negative feedback, which is often overlooked in existing graph-based recommendation systems. The paper highlights that traditional graph neural networks are not well-suited for capturing negative feedback, which acts as a high-frequency signal in user-item graphs. DFGNN addresses this limitation by incorporating dual-frequency graph filters that capture both positive and negative feedback signals .

  2. Signed Graph Regularization: DFGNN utilizes signed graph regularization to maintain the uniformity of user/item embeddings in the embedding space, thereby alleviating the representation degeneration problem commonly observed in recommendation systems. This regularization technique helps improve the overall performance of the model by considering both positive and negative feedback signals .

  3. Performance Comparison: The paper compares DFGNN with classical and state-of-the-art unsigned/signed graph neural networks, such as GCN, GAT, SGCN, SBGNN, SBGCL, and SIGRec. The results show that the signed graph neural network generally outperforms unsigned graph neural networks across all datasets, emphasizing the importance of considering negative feedback. Additionally, DFGNN demonstrates superior performance compared to SGCN, with SBGCL specifically designed for bipartite graphs showing better performance due to its tailored structure matching the characteristics of user-item bipartite graphs .

  4. Experimental Validation: Extensive experiments conducted on real-world datasets validate the effectiveness of DFGNN in capturing both positive and negative feedback signals for improved recommendations. The results demonstrate the superiority of DFGNN over traditional graph neural networks in handling negative feedback and maintaining embedding uniformity .

Overall, the characteristics of DFGNN, such as its focus on modeling negative feedback, utilization of signed graph regularization, and superior performance compared to previous methods, highlight its advancements in addressing the challenges associated with incorporating negative feedback in graph-based recommendation systems.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of graph-based recommendation systems. Noteworthy researchers in this area include Yiqing Wu, Ruobing Xie, Zhao Zhang, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Zhanhui Kang, and Yongjun Xu . These researchers have contributed to the development of the Dual-frequency Graph Neural Network for Sign-aware Feedback (DFGNN) model, which addresses the challenge of incorporating negative feedback in graph-based recommendation systems.

The key to the solution proposed in the paper lies in the development of the Dual-frequency Graph Neural Network (DFGNN) model. This model utilizes a dual-frequency graph filter (DGF) to capture both low-frequency and high-frequency signals containing positive and negative feedback. Additionally, the model applies signed graph regularization to maintain uniform user/item embeddings in the embedding space, thereby alleviating the representation degeneration problem commonly observed in graph-based recommendation systems .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The experiments aimed to analyze the characteristics of negative and positive feedback in graph-based recommendation and the challenges associated with processing signed graphs .
  • The experiments involved conducting comprehensive analyses on real-world datasets such as ML1M, different categories of Amazon Review datasets, and Yelp dataset to evaluate the proposed Dual-frequency Graph Neural Network (DFGNN) .
  • The experiments included comparing the performance of DFGNN with various baselines such as GCN, GAT, SGCN, SBGNN, and SBGCL on recommendation tasks .
  • Ablation experiments were conducted to evaluate the effectiveness of the proposed modules in DFGNN, including Basic, Basic + LGF, Basic + DGF, and the full version of DFGNN .
  • The experiments involved conducting grid searches for hyper-parameters and comparing the performance of DFGNN with other models on feedback type recognition tasks .
  • The experiments also included visualizations of learned embeddings, measuring uniformity, and analyzing the effectiveness of the proposed model in handling negative feedback signals .
  • Extensive experiments were conducted on real-world datasets to assess the effectiveness of DFGNN in two recommendation tasks .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study of the Dual-frequency Graph Neural Network for Sign-aware Feedback (DFGNN) includes classical ML1M, different categories of Amazon Review datasets (ArtsCrafts and Sewing, Grocery and Gourmet Food, Kindle Store), and the Yelp dataset . The statistical information of these datasets, such as the number of users, items, instances, and negative rates, is detailed in Table 3 of the research .

Regarding the code, the study mentions that the codes of the proposed model will be released upon acceptance . This indicates that the code for the Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN) model will be made available for public use after the acceptance of the research work.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted a comprehensive analysis of positive and negative feedback in graph-based recommendation systems, focusing on modeling these feedback types from a frequency perspective . The research delved into the characteristics of positive and negative feedback, highlighting the differences in meanings and the heterogeneous nature of negative feedback . Additionally, the study explored the challenges related to processing signed graphs in recommendations, particularly addressing the representation degeneration problem .

Furthermore, the paper proposed a novel model, the Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN), to effectively model positive and negative feedback in graph-based recommendations . The DFGNN model was designed to capture both low-frequency and high-frequency signals containing positive and negative feedback, addressing the limitations of existing graph neural networks in handling negative feedback . The proposed signed graph regularization was introduced to maintain uniform user/item embeddings in the embedding space, mitigating the representation degeneration issue .

Moreover, extensive experiments were conducted on real-world datasets, including ML1M, Amazon Review datasets, and Yelp dataset, to evaluate the performance of the DFGNN model . The experimental results demonstrated the effectiveness of the proposed model, showcasing improvements over baselines in recommendation tasks . The study's findings indicate that the DFGNN model outperformed both signed and unsigned graph neural networks, highlighting its capability to handle positive and negative feedback in recommendations .

In conclusion, the experiments and results presented in the paper offer strong empirical support for the scientific hypotheses under investigation. The comprehensive analysis, innovative model design, and extensive evaluation on real-world datasets collectively contribute to advancing the understanding and effectiveness of modeling positive and negative feedback in graph-based recommendation systems.


What are the contributions of this paper?

The paper "DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback" makes the following contributions:

  • Proposing a novel model: The paper introduces a novel model called Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN) .
  • Addressing negative feedback: It focuses on utilizing negative feedback in graph-based recommendations, which is often overlooked in existing models .
  • Frequency filter perspective: DFGNN incorporates a dual-frequency graph filter (DGF) to capture both low-frequency and high-frequency signals containing positive and negative feedback .
  • Signed graph regularization: The model applies signed graph regularization to maintain uniform user/item embeddings in the embedding space, addressing the representation degeneration problem .
  • Experimental validation: The paper conducts extensive experiments on real-world datasets to demonstrate the effectiveness of the proposed DFGNN model .

What work can be continued in depth?

Further research can be conducted to delve deeper into modeling negative feedback in graph-based recommendation systems. While existing studies have made progress in this area, there is still room for exploration and improvement . One aspect that can be further investigated is how to effectively incorporate negative feedback signals into graph neural networks to enhance recommendation accuracy and user experience . Additionally, exploring novel graph neural network architectures that specifically address the challenges of modeling negative feedback, such as designing specialized graph filters and regularization techniques, could be a promising direction for future research .


Introduction
Background
Importance of negative feedback in recommendation systems
Current models' reliance on positive interactions
Challenges with graph-based recommendation systems and negative feedback
Objective
To investigate underutilization of negative feedback in GNNs
To propose a solution for modeling both positive and negative signals
To enhance recommendation quality through improved representation
Method
Data Collection
Real-world datasets: description and characteristics
Data preprocessing: handling imbalance and noise
Data Preprocessing
Feature extraction from signed graphs
Node and edge representation for DFGNN
Dual-frequency Graph Neural Network (DFGNN)
Architecture
Dual-frequency graph filter design
Integration of positive and negative signals
Signed graph regularization mechanism
Training and Optimization
Loss function for sign-aware learning
Training procedure and hyperparameter tuning
Evaluation
Baseline models: selection and comparison
Performance metrics: precision, recall, and AUC-PR
Experiment design and results
Results and Discussion
Impact of DFGNN on recommendation quality
Representation degeneration analysis
Significance of considering both positive and negative feedback
Conclusion
Contribution of DFGNN to graph-based recommendation systems
Limitations and future research directions
Practical implications for real-world applications
Future Work
Extension to other domains and datasets
Integration with online learning and dynamic graphs
Comparison with state-of-the-art methods in real-time scenarios
Basic info
papers
information retrieval
machine learning
artificial intelligence
Advanced features
Insights
What is the primary contribution of the Dual-frequency Graph Neural Network (DFGNN) proposed in the paper?
How does DFGNN improve recommendation quality compared to baseline models, as shown in the experiments?
What does the paper focus on in the context of graph-based recommendation systems?
What problem does the investigated paper claim existing models have with respect to negative feedback?

DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback

Yiqing Wu, Ruobing Xie, Zhao Zhang, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Zhanhui Kang, Yongjun Xu·May 24, 2024

Summary

The paper investigates the underutilization of negative feedback in graph-based recommendation systems, where existing models often rely on positive interactions. It reveals that current graph neural networks struggle to model negative feedback and suffer from representation degeneration, impacting recommendation quality. To address these issues, the authors propose Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN), which incorporates a dual-frequency graph filter to capture both positive and negative signals and a signed graph regularization to maintain consistent user/item embeddings. DFGNN outperforms baseline models in handling negative feedback and improving recommendation tasks, as demonstrated by experiments on real-world datasets. The study highlights the importance of considering both positive and negative feedback in recommendation systems and the effectiveness of DFGNN in capturing complex interactions.
Mind map
Training procedure and hyperparameter tuning
Loss function for sign-aware learning
Signed graph regularization mechanism
Integration of positive and negative signals
Dual-frequency graph filter design
Experiment design and results
Performance metrics: precision, recall, and AUC-PR
Baseline models: selection and comparison
Training and Optimization
Architecture
Node and edge representation for DFGNN
Feature extraction from signed graphs
Data preprocessing: handling imbalance and noise
Real-world datasets: description and characteristics
To enhance recommendation quality through improved representation
To propose a solution for modeling both positive and negative signals
To investigate underutilization of negative feedback in GNNs
Challenges with graph-based recommendation systems and negative feedback
Current models' reliance on positive interactions
Importance of negative feedback in recommendation systems
Comparison with state-of-the-art methods in real-time scenarios
Integration with online learning and dynamic graphs
Extension to other domains and datasets
Practical implications for real-world applications
Limitations and future research directions
Contribution of DFGNN to graph-based recommendation systems
Significance of considering both positive and negative feedback
Representation degeneration analysis
Impact of DFGNN on recommendation quality
Evaluation
Dual-frequency Graph Neural Network (DFGNN)
Data Preprocessing
Data Collection
Objective
Background
Future Work
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Importance of negative feedback in recommendation systems
Current models' reliance on positive interactions
Challenges with graph-based recommendation systems and negative feedback
Objective
To investigate underutilization of negative feedback in GNNs
To propose a solution for modeling both positive and negative signals
To enhance recommendation quality through improved representation
Method
Data Collection
Real-world datasets: description and characteristics
Data preprocessing: handling imbalance and noise
Data Preprocessing
Feature extraction from signed graphs
Node and edge representation for DFGNN
Dual-frequency Graph Neural Network (DFGNN)
Architecture
Dual-frequency graph filter design
Integration of positive and negative signals
Signed graph regularization mechanism
Training and Optimization
Loss function for sign-aware learning
Training procedure and hyperparameter tuning
Evaluation
Baseline models: selection and comparison
Performance metrics: precision, recall, and AUC-PR
Experiment design and results
Results and Discussion
Impact of DFGNN on recommendation quality
Representation degeneration analysis
Significance of considering both positive and negative feedback
Conclusion
Contribution of DFGNN to graph-based recommendation systems
Limitations and future research directions
Practical implications for real-world applications
Future Work
Extension to other domains and datasets
Integration with online learning and dynamic graphs
Comparison with state-of-the-art methods in real-time scenarios

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper addresses two main problems in graph-based recommendation systems:

  1. Negative Feedback Modeling: The paper aims to tackle the challenge of effectively modeling negative feedback in graph-based recommendation systems. Negative feedback, such as user dislikes or low ratings, is crucial for improving recommendation accuracy and user satisfaction . This problem is not entirely new, as existing graph neural networks (GNNs) have struggled to adequately capture negative feedback, which acts as a high-frequency signal in user-item graphs .
  2. Representation Degeneration: The paper also focuses on the representation degeneration problem in graph-based recommendations. This issue arises due to the over-smoothing effect in GNNs, where node features become overly similar during training, leading to a loss of expressive power in the embeddings . While this problem has been identified in previous studies , the paper proposes a novel solution to alleviate representation degeneration by introducing a signed-graph regularization loss in the Dual-frequency Graph Neural Network (DFGNN) model .

In summary, the paper aims to address the challenges of negative feedback modeling and representation degeneration in graph-based recommendation systems, providing innovative solutions to enhance the performance and effectiveness of these systems.


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis that existing graph neural networks are not well-suited for modeling negative feedback, which acts as a high-frequency signal in a user-item graph, and that the graph-based recommendation suffers from the representation degeneration problem . The study aims to address the challenge of incorporating negative feedback, such as dislikes and low ratings, into graph-based recommendation systems, which has been underexplored in current research .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback" introduces several novel ideas, methods, and models in the field of graph-based recommendation systems . Here are the key contributions of the paper:

  1. Dual-frequency Graph Neural Network (DFGNN): The paper proposes the DFGNN model, which addresses the challenge of modeling both positive and negative feedback in graph-based recommendation systems. DFGNN utilizes a dual-frequency graph filter (DGF) to capture low-frequency and high-frequency signals representing positive and negative feedback, respectively. This approach allows for a more comprehensive analysis of user-item interactions .

  2. Signed Graph Regularization: To maintain the uniformity of user/item embeddings in the embedding space and alleviate representation degeneration issues, the paper introduces signed graph regularization. This regularization technique helps in improving the overall performance of the recommendation model by considering both positive and negative feedback signals .

  3. Analysis of Negative and Positive Feedback: The paper conducts an in-depth analysis of negative and positive feedback from a graph signal frequency perspective. This analysis forms the basis for the development of the DFGNN model and highlights the importance of considering both types of feedback for more effective recommendation systems .

  4. Experimental Validation: Extensive experiments are conducted on real-world datasets to validate the effectiveness of the proposed DFGNN model. The results demonstrate the power of DFGNN in capturing and utilizing both positive and negative feedback signals for improved recommendations .

Overall, the paper introduces a novel approach to graph-based recommendation systems by proposing the DFGNN model, incorporating dual-frequency graph filters and signed graph regularization to address the challenges associated with modeling negative feedback and representation degeneration in recommendation scenarios. The "DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback" paper introduces several key characteristics and advantages compared to previous methods in the field of graph-based recommendation systems .

  1. Modeling Negative Feedback: One of the main characteristics of DFGNN is its ability to effectively model negative feedback, which is often overlooked in existing graph-based recommendation systems. The paper highlights that traditional graph neural networks are not well-suited for capturing negative feedback, which acts as a high-frequency signal in user-item graphs. DFGNN addresses this limitation by incorporating dual-frequency graph filters that capture both positive and negative feedback signals .

  2. Signed Graph Regularization: DFGNN utilizes signed graph regularization to maintain the uniformity of user/item embeddings in the embedding space, thereby alleviating the representation degeneration problem commonly observed in recommendation systems. This regularization technique helps improve the overall performance of the model by considering both positive and negative feedback signals .

  3. Performance Comparison: The paper compares DFGNN with classical and state-of-the-art unsigned/signed graph neural networks, such as GCN, GAT, SGCN, SBGNN, SBGCL, and SIGRec. The results show that the signed graph neural network generally outperforms unsigned graph neural networks across all datasets, emphasizing the importance of considering negative feedback. Additionally, DFGNN demonstrates superior performance compared to SGCN, with SBGCL specifically designed for bipartite graphs showing better performance due to its tailored structure matching the characteristics of user-item bipartite graphs .

  4. Experimental Validation: Extensive experiments conducted on real-world datasets validate the effectiveness of DFGNN in capturing both positive and negative feedback signals for improved recommendations. The results demonstrate the superiority of DFGNN over traditional graph neural networks in handling negative feedback and maintaining embedding uniformity .

Overall, the characteristics of DFGNN, such as its focus on modeling negative feedback, utilization of signed graph regularization, and superior performance compared to previous methods, highlight its advancements in addressing the challenges associated with incorporating negative feedback in graph-based recommendation systems.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of graph-based recommendation systems. Noteworthy researchers in this area include Yiqing Wu, Ruobing Xie, Zhao Zhang, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Zhanhui Kang, and Yongjun Xu . These researchers have contributed to the development of the Dual-frequency Graph Neural Network for Sign-aware Feedback (DFGNN) model, which addresses the challenge of incorporating negative feedback in graph-based recommendation systems.

The key to the solution proposed in the paper lies in the development of the Dual-frequency Graph Neural Network (DFGNN) model. This model utilizes a dual-frequency graph filter (DGF) to capture both low-frequency and high-frequency signals containing positive and negative feedback. Additionally, the model applies signed graph regularization to maintain uniform user/item embeddings in the embedding space, thereby alleviating the representation degeneration problem commonly observed in graph-based recommendation systems .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The experiments aimed to analyze the characteristics of negative and positive feedback in graph-based recommendation and the challenges associated with processing signed graphs .
  • The experiments involved conducting comprehensive analyses on real-world datasets such as ML1M, different categories of Amazon Review datasets, and Yelp dataset to evaluate the proposed Dual-frequency Graph Neural Network (DFGNN) .
  • The experiments included comparing the performance of DFGNN with various baselines such as GCN, GAT, SGCN, SBGNN, and SBGCL on recommendation tasks .
  • Ablation experiments were conducted to evaluate the effectiveness of the proposed modules in DFGNN, including Basic, Basic + LGF, Basic + DGF, and the full version of DFGNN .
  • The experiments involved conducting grid searches for hyper-parameters and comparing the performance of DFGNN with other models on feedback type recognition tasks .
  • The experiments also included visualizations of learned embeddings, measuring uniformity, and analyzing the effectiveness of the proposed model in handling negative feedback signals .
  • Extensive experiments were conducted on real-world datasets to assess the effectiveness of DFGNN in two recommendation tasks .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study of the Dual-frequency Graph Neural Network for Sign-aware Feedback (DFGNN) includes classical ML1M, different categories of Amazon Review datasets (ArtsCrafts and Sewing, Grocery and Gourmet Food, Kindle Store), and the Yelp dataset . The statistical information of these datasets, such as the number of users, items, instances, and negative rates, is detailed in Table 3 of the research .

Regarding the code, the study mentions that the codes of the proposed model will be released upon acceptance . This indicates that the code for the Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN) model will be made available for public use after the acceptance of the research work.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted a comprehensive analysis of positive and negative feedback in graph-based recommendation systems, focusing on modeling these feedback types from a frequency perspective . The research delved into the characteristics of positive and negative feedback, highlighting the differences in meanings and the heterogeneous nature of negative feedback . Additionally, the study explored the challenges related to processing signed graphs in recommendations, particularly addressing the representation degeneration problem .

Furthermore, the paper proposed a novel model, the Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN), to effectively model positive and negative feedback in graph-based recommendations . The DFGNN model was designed to capture both low-frequency and high-frequency signals containing positive and negative feedback, addressing the limitations of existing graph neural networks in handling negative feedback . The proposed signed graph regularization was introduced to maintain uniform user/item embeddings in the embedding space, mitigating the representation degeneration issue .

Moreover, extensive experiments were conducted on real-world datasets, including ML1M, Amazon Review datasets, and Yelp dataset, to evaluate the performance of the DFGNN model . The experimental results demonstrated the effectiveness of the proposed model, showcasing improvements over baselines in recommendation tasks . The study's findings indicate that the DFGNN model outperformed both signed and unsigned graph neural networks, highlighting its capability to handle positive and negative feedback in recommendations .

In conclusion, the experiments and results presented in the paper offer strong empirical support for the scientific hypotheses under investigation. The comprehensive analysis, innovative model design, and extensive evaluation on real-world datasets collectively contribute to advancing the understanding and effectiveness of modeling positive and negative feedback in graph-based recommendation systems.


What are the contributions of this paper?

The paper "DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback" makes the following contributions:

  • Proposing a novel model: The paper introduces a novel model called Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN) .
  • Addressing negative feedback: It focuses on utilizing negative feedback in graph-based recommendations, which is often overlooked in existing models .
  • Frequency filter perspective: DFGNN incorporates a dual-frequency graph filter (DGF) to capture both low-frequency and high-frequency signals containing positive and negative feedback .
  • Signed graph regularization: The model applies signed graph regularization to maintain uniform user/item embeddings in the embedding space, addressing the representation degeneration problem .
  • Experimental validation: The paper conducts extensive experiments on real-world datasets to demonstrate the effectiveness of the proposed DFGNN model .

What work can be continued in depth?

Further research can be conducted to delve deeper into modeling negative feedback in graph-based recommendation systems. While existing studies have made progress in this area, there is still room for exploration and improvement . One aspect that can be further investigated is how to effectively incorporate negative feedback signals into graph neural networks to enhance recommendation accuracy and user experience . Additionally, exploring novel graph neural network architectures that specifically address the challenges of modeling negative feedback, such as designing specialized graph filters and regularization techniques, could be a promising direction for future research .

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