Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop

Yuqi Zhou, Sunhao Dai, Liang Pang, Gang Wang, Zhenhua Dong, Jun Xu, Ji-Rong Wen·May 28, 2024

Summary

This study investigates the escalating source bias in recommendation systems, particularly with the integration of Artificial Intelligence Generated Content (AIGC) in neural models. It identifies three stages of AIGC integration: HGC dominance, coexistence, and dominance, where bias increases. The research finds that AIGC is disproportionately recommended, contributing to a digital echo chamber. To address this, a black-box debiasing method is introduced, which successfully disrupts the feedback loop and reduces source bias. The study highlights the need to maintain model impartiality as LLMs become more prevalent in recommendations, and the impact of AIGC on model performance and fairness is examined through various models and datasets.

Key findings

6

Paper digest

Q1. What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of source bias in recommender systems, specifically focusing on the impact of Artificial Intelligence Generated Content (AIGC) on the feedback loop of recommender systems . This problem is not entirely new, as previous studies have identified source bias in retrieval systems . However, the paper extends the investigation into recommender systems, examining how source bias affects neural recommendation models within the feedback loop across different phases . The study highlights the amplification of source bias throughout the feedback loop due to the integration of AIGC, emphasizing the need to disrupt the propagation of source bias in the feedback loop mechanism . The paper introduces a novel debiasing method to prevent the escalation and amplification of source bias during the influx of AIGC in the feedback loop, aiming to maintain bias within acceptable limits and ensure the neutrality of model predictions .


Q2. What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis regarding the impact of source bias, particularly Artificial Intelligence Generated Content (AIGC), on recommender systems within the feedback loop involving users, data, and the recommender system . The study explores how source bias, such as the preference for AIGC by neural retrieval models, affects recommendation models at different phases of the feedback loop . The research delves into the integration of AIGC into the recommendation content ecosystem across past, present, and future states, highlighting the prevalence of source bias and the potential creation of a digital echo chamber amplifying this bias throughout the feedback loop .


Q3. What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop" proposes several new ideas, methods, and models related to combating biases in recommender systems and addressing challenges posed by Artificial Intelligence Generated Content (AIGC) .

  1. Debiasing Methods: The paper introduces debiasing methods from three perspectives to address biases in recommender systems. Firstly, it proposes a debiasing constraint on both item representation and history representation. Secondly, it utilizes a black-box strategy to focus on the differences between the rewritten text before and after without requiring knowledge of the training text source. Thirdly, it replaces hinge loss with L1 loss to ensure the model does not favor either Human Generated Content (HGC) or AIGC .

  2. Feedback Loop Training Algorithm: The paper outlines a feedback loop for model training that involves interactions with datasets, feedback loop iterations, and specific parameters. It emphasizes the importance of training models within a feedback loop to address biases and improve the performance of recommendation models .

  3. Impact of AIGC on Recommender Systems: The paper investigates the effects of AIGC on recommender systems, particularly focusing on the changes and influences of source bias in the feedback loop of recommender systems. It highlights the challenges that the development of Large Language Models (LLMs) may pose to recommender systems and explores how recommender systems can benefit from LLMs .

  4. Neural Retrievers and Bias: The paper discusses how neural retrieval models tend to favor AIGC and rank them higher in text and image retrieval systems. It addresses the bias towards LLM-generated content and the implications of this bias on information retrieval systems .

In summary, the paper introduces innovative debiasing methods, feedback loop training algorithms, and explores the impact of AIGC on recommender systems, providing valuable insights into addressing biases and challenges associated with the proliferation of AIGC in online content . The paper "Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop" introduces novel debiasing methods that offer distinct characteristics and advantages compared to previous approaches .

  1. Debiasing Constraint: The proposed debiasing method includes a constraint on both item representation and history representation, ensuring a comprehensive approach to mitigating biases in recommender systems. This constraint aims to address biases at different levels within the system, enhancing the overall effectiveness of the debiasing process .

  2. Black-Box Strategy: The paper utilizes a black-box strategy that focuses on the differences between the rewritten text before and after without requiring knowledge of the source of the training text. This strategy enhances the efficiency of the debiasing process by directly targeting the changes introduced by the rewriting process, thereby improving the model's impartiality towards both Human Generated Content (HGC) and Artificial Intelligence Generated Content (AIGC) .

  3. Replacement of Hinge Loss with L1 Loss: By replacing the hinge loss with the L1 loss function, the proposed method ensures that the model does not favor either HGC or AIGC during the feedback loop training. This adjustment helps maintain model neutrality and prevents the gradual development of new biases, thereby improving the overall fairness and performance of the recommender system .

  4. Performance Validation: The experimental results presented in the paper demonstrate the effectiveness of the proposed debiasing method in disrupting the feedback loop and countering bias escalation. By validating the performance of the debiasing method, the paper highlights its potential to address source bias and enhance the impartiality of recommender systems towards different types of content .

In summary, the characteristics of the proposed debiasing methods include a comprehensive constraint approach, a targeted black-box strategy, and the use of L1 loss to maintain model impartiality. These characteristics offer advantages in addressing biases in recommender systems and preventing the escalation of source bias within the feedback loop, as demonstrated through experimental validation .


Q4. 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 papers exist in the field of exploring source bias in user, data, and recommender system feedback loops. Noteworthy researchers in this area include Yuqi Zhou, Sunhao Dai, Liang Pang, Gang Wang, Zhenhua Dong, and Jun Xu . These researchers have delved into investigating the impact of artificial intelligence-generated content (AIGC) on recommender systems, particularly focusing on the changes and influences of source bias within the feedback loop of recommender systems .

The key to the solution mentioned in the paper involves extending the investigation of source bias into recommender systems, specifically examining its impact across different phases of the feedback loop. The study conceptualizes the progression of AIGC integration into the recommendation content ecosystem in three distinct phases - HGC dominance, HGC-AIGC co-existence, and AIGC dominance, representing past, present, and future states, respectively. Through extensive experiments across diverse datasets, the prevalence of source bias is demonstrated, highlighting a potential digital echo chamber with source bias amplification throughout the feedback loop .


Q5. How were the experiments in the paper designed?

The experiments in the paper were designed with the following key aspects:

  • Datasets: The experiments were conducted on real-world datasets from Amazon, focusing on three categories: "Health", "Beauty", and "Sports". The datasets comprised product reviews and descriptions, with top-level product categories treated as separate datasets .
  • Recommendation Models: Four representative recommendation models were selected for the experiments: BERT4Rec, SASRec, GRU4Rec, and LRURec. These models have different architectures and approaches for sequential recommendation tasks .
  • Experimental Setup: The pre-trained language models were frozen for computational efficiency. The recommendation models were trained for 5 epochs, with the best-performing model selected for testing. Specific parameters like batch size, learning rate, item vector dimension, and score calculations were set for training consistency .
  • Debiasing Methods: The experiments included debiasing methods to counteract source bias in the feedback loop. These methods involved debiasing constraints on item and history representations, a black-box strategy to focus on text differences, and the use of L1 loss instead of hinge loss to prevent favoritism towards HGC or AIGC .
  • Evaluation: The experiments aimed to validate the effectiveness of the proposed debiasing method in disrupting the feedback loop and maintaining model impartiality towards both HGC and AIGC. The results confirmed the potential of the debiasing method to mitigate bias escalation .

Q6. What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is a collection of three datasets from diverse domains, including Health, Beauty, and Sports . The code for the study is not explicitly mentioned to be open source in the provided context .


Q7. 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 strong support for the scientific hypotheses that needed to be verified. The study delves into the impact of source bias in recommender systems across different phases of the feedback loop . Through extensive experiments conducted on three datasets from diverse domains, the prevalence of source bias is demonstrated, highlighting a potential digital echo chamber with source bias amplification throughout the feedback loop . The results show that most recommendation models exhibit a preference for AI-generated content (AIGC) in terms of metrics such as NDCG@K and MAP@K, indicating a bias towards AIGC . This bias is further confirmed by testing recommendation models on AIGC generated by popular large language models (LLMs) like ChatGPT, Llama2, Mistral, and Gemini-Pro, which show varying degrees of source bias, reinforcing the significance of this phenomenon . Additionally, the study validates the existence of source bias in recommender systems during the phase where human-generated content dominates, further supporting the hypothesis of source bias amplification in the feedback loop . The comprehensive analysis and experimental results presented in the paper provide robust evidence to support the scientific hypotheses related to source bias in recommender systems.


Q8. What are the contributions of this paper?

The paper "Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop" makes several contributions:

  • It investigates the impact of source bias on neural recommendation models within the feedback loop involving users, data, and recommender systems .
  • The study extends the investigation of source bias into recommender systems, focusing on its effects across different phases of the feedback loop .
  • Through extensive experiments across diverse datasets, the paper demonstrates the prevalence of source bias and highlights the potential creation of a digital echo chamber with source bias amplification throughout the feedback loop .
  • The research reveals the progression of AI-generated content (AIGC) integration into the recommendation content ecosystem in three phases: HGC dominance, HGC-AIGC co-existence, and AIGC dominance, representing past, present, and future states, respectively .
  • It emphasizes the importance of countering source bias to prevent a recommender ecosystem where limited information sources, such as AIGC, are disproportionately recommended .

Q9. What work can be continued in depth?

Further research can be conducted to delve deeper into the impact of source bias on neural recommendation models within the feedback loop . This includes exploring how source bias affects different phases of the feedback loop, such as the HGC dominate, HGC-AIGC coexist, and AIGC dominate phases . Additionally, investigating the effectiveness of debiasing methods in mitigating and preventing the amplification of source bias throughout the feedback loop mechanism could be a valuable area for continued study .

Tables

2

Introduction
Background
[Rise of AI Generated Content (AIGC) in recommendation systems]
[Impact of AIGC on recommendation dynamics]
Objective
[Investigation of AIGC integration stages and bias escalation]
[Development of a black-box debiasing method]
[Analysis of model impartiality and fairness implications]
Methodology
Data Collection
[Selection of datasets with AIGC and human-generated content]
[Data collection from various recommendation platforms]
Data Preprocessing
[Categorization of AIGC integration stages (HGC, coexistence, dominance)]
[Data cleaning and standardization for analysis]
Bias Analysis
[Quantification of source bias in recommendation patterns]
[Comparison of bias across different integration stages]
Black-Box Debiasing Method
[Description of the method]
[Implementation and evaluation criteria]
Model Performance and Fairness
[Impact of debiasing on model accuracy]
[Fairness metrics: diversity, exposure, and representation]
[Experiments with various neural models]
Results
[Observations on bias trends with AIGC integration]
[Debiasing method's effectiveness in reducing source bias]
[Trade-offs between fairness and model performance]
Discussion
[Implications of source bias on user experience and echo chambers]
[The role of platform responsibility and regulation]
[Future directions for mitigating AIGC bias in recommendations]
Conclusion
[Summary of key findings]
[Importance of addressing AIGC bias for recommendation system ethics]
[Call to action for industry and research community]
Basic info
papers
computation and language
information retrieval
artificial intelligence
Advanced features
Insights
How does the integration of Artificial Intelligence Generated Content (AIGC) affect bias in neural models, as described in the stages mentioned?
What problem does the black-box debiasing method aim to solve regarding AIGC in recommendation systems?
What implications does the study have for maintaining model impartiality with the increasing prevalence of LLMs?
What is the primary focus of the study in terms of recommendation systems?

Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop

Yuqi Zhou, Sunhao Dai, Liang Pang, Gang Wang, Zhenhua Dong, Jun Xu, Ji-Rong Wen·May 28, 2024

Summary

This study investigates the escalating source bias in recommendation systems, particularly with the integration of Artificial Intelligence Generated Content (AIGC) in neural models. It identifies three stages of AIGC integration: HGC dominance, coexistence, and dominance, where bias increases. The research finds that AIGC is disproportionately recommended, contributing to a digital echo chamber. To address this, a black-box debiasing method is introduced, which successfully disrupts the feedback loop and reduces source bias. The study highlights the need to maintain model impartiality as LLMs become more prevalent in recommendations, and the impact of AIGC on model performance and fairness is examined through various models and datasets.
Mind map
[Implementation and evaluation criteria]
[Description of the method]
[Experiments with various neural models]
[Fairness metrics: diversity, exposure, and representation]
[Impact of debiasing on model accuracy]
Black-Box Debiasing Method
[Data cleaning and standardization for analysis]
[Categorization of AIGC integration stages (HGC, coexistence, dominance)]
[Data collection from various recommendation platforms]
[Selection of datasets with AIGC and human-generated content]
[Analysis of model impartiality and fairness implications]
[Development of a black-box debiasing method]
[Investigation of AIGC integration stages and bias escalation]
[Impact of AIGC on recommendation dynamics]
[Rise of AI Generated Content (AIGC) in recommendation systems]
[Call to action for industry and research community]
[Importance of addressing AIGC bias for recommendation system ethics]
[Summary of key findings]
[Future directions for mitigating AIGC bias in recommendations]
[The role of platform responsibility and regulation]
[Implications of source bias on user experience and echo chambers]
[Trade-offs between fairness and model performance]
[Debiasing method's effectiveness in reducing source bias]
[Observations on bias trends with AIGC integration]
Model Performance and Fairness
Bias Analysis
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Results
Methodology
Introduction
Outline
Introduction
Background
[Rise of AI Generated Content (AIGC) in recommendation systems]
[Impact of AIGC on recommendation dynamics]
Objective
[Investigation of AIGC integration stages and bias escalation]
[Development of a black-box debiasing method]
[Analysis of model impartiality and fairness implications]
Methodology
Data Collection
[Selection of datasets with AIGC and human-generated content]
[Data collection from various recommendation platforms]
Data Preprocessing
[Categorization of AIGC integration stages (HGC, coexistence, dominance)]
[Data cleaning and standardization for analysis]
Bias Analysis
[Quantification of source bias in recommendation patterns]
[Comparison of bias across different integration stages]
Black-Box Debiasing Method
[Description of the method]
[Implementation and evaluation criteria]
Model Performance and Fairness
[Impact of debiasing on model accuracy]
[Fairness metrics: diversity, exposure, and representation]
[Experiments with various neural models]
Results
[Observations on bias trends with AIGC integration]
[Debiasing method's effectiveness in reducing source bias]
[Trade-offs between fairness and model performance]
Discussion
[Implications of source bias on user experience and echo chambers]
[The role of platform responsibility and regulation]
[Future directions for mitigating AIGC bias in recommendations]
Conclusion
[Summary of key findings]
[Importance of addressing AIGC bias for recommendation system ethics]
[Call to action for industry and research community]
Key findings
6

Paper digest

Q1. What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of source bias in recommender systems, specifically focusing on the impact of Artificial Intelligence Generated Content (AIGC) on the feedback loop of recommender systems . This problem is not entirely new, as previous studies have identified source bias in retrieval systems . However, the paper extends the investigation into recommender systems, examining how source bias affects neural recommendation models within the feedback loop across different phases . The study highlights the amplification of source bias throughout the feedback loop due to the integration of AIGC, emphasizing the need to disrupt the propagation of source bias in the feedback loop mechanism . The paper introduces a novel debiasing method to prevent the escalation and amplification of source bias during the influx of AIGC in the feedback loop, aiming to maintain bias within acceptable limits and ensure the neutrality of model predictions .


Q2. What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis regarding the impact of source bias, particularly Artificial Intelligence Generated Content (AIGC), on recommender systems within the feedback loop involving users, data, and the recommender system . The study explores how source bias, such as the preference for AIGC by neural retrieval models, affects recommendation models at different phases of the feedback loop . The research delves into the integration of AIGC into the recommendation content ecosystem across past, present, and future states, highlighting the prevalence of source bias and the potential creation of a digital echo chamber amplifying this bias throughout the feedback loop .


Q3. What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop" proposes several new ideas, methods, and models related to combating biases in recommender systems and addressing challenges posed by Artificial Intelligence Generated Content (AIGC) .

  1. Debiasing Methods: The paper introduces debiasing methods from three perspectives to address biases in recommender systems. Firstly, it proposes a debiasing constraint on both item representation and history representation. Secondly, it utilizes a black-box strategy to focus on the differences between the rewritten text before and after without requiring knowledge of the training text source. Thirdly, it replaces hinge loss with L1 loss to ensure the model does not favor either Human Generated Content (HGC) or AIGC .

  2. Feedback Loop Training Algorithm: The paper outlines a feedback loop for model training that involves interactions with datasets, feedback loop iterations, and specific parameters. It emphasizes the importance of training models within a feedback loop to address biases and improve the performance of recommendation models .

  3. Impact of AIGC on Recommender Systems: The paper investigates the effects of AIGC on recommender systems, particularly focusing on the changes and influences of source bias in the feedback loop of recommender systems. It highlights the challenges that the development of Large Language Models (LLMs) may pose to recommender systems and explores how recommender systems can benefit from LLMs .

  4. Neural Retrievers and Bias: The paper discusses how neural retrieval models tend to favor AIGC and rank them higher in text and image retrieval systems. It addresses the bias towards LLM-generated content and the implications of this bias on information retrieval systems .

In summary, the paper introduces innovative debiasing methods, feedback loop training algorithms, and explores the impact of AIGC on recommender systems, providing valuable insights into addressing biases and challenges associated with the proliferation of AIGC in online content . The paper "Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop" introduces novel debiasing methods that offer distinct characteristics and advantages compared to previous approaches .

  1. Debiasing Constraint: The proposed debiasing method includes a constraint on both item representation and history representation, ensuring a comprehensive approach to mitigating biases in recommender systems. This constraint aims to address biases at different levels within the system, enhancing the overall effectiveness of the debiasing process .

  2. Black-Box Strategy: The paper utilizes a black-box strategy that focuses on the differences between the rewritten text before and after without requiring knowledge of the source of the training text. This strategy enhances the efficiency of the debiasing process by directly targeting the changes introduced by the rewriting process, thereby improving the model's impartiality towards both Human Generated Content (HGC) and Artificial Intelligence Generated Content (AIGC) .

  3. Replacement of Hinge Loss with L1 Loss: By replacing the hinge loss with the L1 loss function, the proposed method ensures that the model does not favor either HGC or AIGC during the feedback loop training. This adjustment helps maintain model neutrality and prevents the gradual development of new biases, thereby improving the overall fairness and performance of the recommender system .

  4. Performance Validation: The experimental results presented in the paper demonstrate the effectiveness of the proposed debiasing method in disrupting the feedback loop and countering bias escalation. By validating the performance of the debiasing method, the paper highlights its potential to address source bias and enhance the impartiality of recommender systems towards different types of content .

In summary, the characteristics of the proposed debiasing methods include a comprehensive constraint approach, a targeted black-box strategy, and the use of L1 loss to maintain model impartiality. These characteristics offer advantages in addressing biases in recommender systems and preventing the escalation of source bias within the feedback loop, as demonstrated through experimental validation .


Q4. 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 papers exist in the field of exploring source bias in user, data, and recommender system feedback loops. Noteworthy researchers in this area include Yuqi Zhou, Sunhao Dai, Liang Pang, Gang Wang, Zhenhua Dong, and Jun Xu . These researchers have delved into investigating the impact of artificial intelligence-generated content (AIGC) on recommender systems, particularly focusing on the changes and influences of source bias within the feedback loop of recommender systems .

The key to the solution mentioned in the paper involves extending the investigation of source bias into recommender systems, specifically examining its impact across different phases of the feedback loop. The study conceptualizes the progression of AIGC integration into the recommendation content ecosystem in three distinct phases - HGC dominance, HGC-AIGC co-existence, and AIGC dominance, representing past, present, and future states, respectively. Through extensive experiments across diverse datasets, the prevalence of source bias is demonstrated, highlighting a potential digital echo chamber with source bias amplification throughout the feedback loop .


Q5. How were the experiments in the paper designed?

The experiments in the paper were designed with the following key aspects:

  • Datasets: The experiments were conducted on real-world datasets from Amazon, focusing on three categories: "Health", "Beauty", and "Sports". The datasets comprised product reviews and descriptions, with top-level product categories treated as separate datasets .
  • Recommendation Models: Four representative recommendation models were selected for the experiments: BERT4Rec, SASRec, GRU4Rec, and LRURec. These models have different architectures and approaches for sequential recommendation tasks .
  • Experimental Setup: The pre-trained language models were frozen for computational efficiency. The recommendation models were trained for 5 epochs, with the best-performing model selected for testing. Specific parameters like batch size, learning rate, item vector dimension, and score calculations were set for training consistency .
  • Debiasing Methods: The experiments included debiasing methods to counteract source bias in the feedback loop. These methods involved debiasing constraints on item and history representations, a black-box strategy to focus on text differences, and the use of L1 loss instead of hinge loss to prevent favoritism towards HGC or AIGC .
  • Evaluation: The experiments aimed to validate the effectiveness of the proposed debiasing method in disrupting the feedback loop and maintaining model impartiality towards both HGC and AIGC. The results confirmed the potential of the debiasing method to mitigate bias escalation .

Q6. What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is a collection of three datasets from diverse domains, including Health, Beauty, and Sports . The code for the study is not explicitly mentioned to be open source in the provided context .


Q7. 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 strong support for the scientific hypotheses that needed to be verified. The study delves into the impact of source bias in recommender systems across different phases of the feedback loop . Through extensive experiments conducted on three datasets from diverse domains, the prevalence of source bias is demonstrated, highlighting a potential digital echo chamber with source bias amplification throughout the feedback loop . The results show that most recommendation models exhibit a preference for AI-generated content (AIGC) in terms of metrics such as NDCG@K and MAP@K, indicating a bias towards AIGC . This bias is further confirmed by testing recommendation models on AIGC generated by popular large language models (LLMs) like ChatGPT, Llama2, Mistral, and Gemini-Pro, which show varying degrees of source bias, reinforcing the significance of this phenomenon . Additionally, the study validates the existence of source bias in recommender systems during the phase where human-generated content dominates, further supporting the hypothesis of source bias amplification in the feedback loop . The comprehensive analysis and experimental results presented in the paper provide robust evidence to support the scientific hypotheses related to source bias in recommender systems.


Q8. What are the contributions of this paper?

The paper "Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop" makes several contributions:

  • It investigates the impact of source bias on neural recommendation models within the feedback loop involving users, data, and recommender systems .
  • The study extends the investigation of source bias into recommender systems, focusing on its effects across different phases of the feedback loop .
  • Through extensive experiments across diverse datasets, the paper demonstrates the prevalence of source bias and highlights the potential creation of a digital echo chamber with source bias amplification throughout the feedback loop .
  • The research reveals the progression of AI-generated content (AIGC) integration into the recommendation content ecosystem in three phases: HGC dominance, HGC-AIGC co-existence, and AIGC dominance, representing past, present, and future states, respectively .
  • It emphasizes the importance of countering source bias to prevent a recommender ecosystem where limited information sources, such as AIGC, are disproportionately recommended .

Q9. What work can be continued in depth?

Further research can be conducted to delve deeper into the impact of source bias on neural recommendation models within the feedback loop . This includes exploring how source bias affects different phases of the feedback loop, such as the HGC dominate, HGC-AIGC coexist, and AIGC dominate phases . Additionally, investigating the effectiveness of debiasing methods in mitigating and preventing the amplification of source bias throughout the feedback loop mechanism could be a valuable area for continued study .

Tables
2
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