DELRec: Distilling Sequential Pattern to Enhance LLM-based Recommendation

Guohao Sun, Haoyi Zhang·June 17, 2024

Summary

The paper presents DELRec, a novel framework that enhances large language models (LLMs) for sequential recommendation by distilling sequential patterns from conventional recommendation models. It addresses the limitations of existing methods by using soft prompts and two designed strategies to teach LLMs about item context and semantic information. The framework combines hard and soft prompts, with a focus on capturing recommendation behavior and temporal dynamics. Experiments on three datasets show DELRec's effectiveness, outperforming baseline models in terms of recommendation accuracy. Key contributions include a parameter-efficient method, improved LLM-based recommendations, and the demonstration of enhanced understanding of item context. The study highlights the potential of integrating sequential patterns into LLMs for more accurate and adaptable recommendations.

Key findings

7

Paper digest

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

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide me with the title of the paper or a brief description of its topic so that I can assist you better.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that the proposed framework, DELRec, enhances the performance of Large Language Models (LLMs) in Sequential Recommendation (SR) tasks by extracting behavioral patterns from conventional SR models and enabling LLMs to effectively utilize this supplementary information for more accurate and effective sequential recommendations . The hypothesis is centered around the idea that by distilling knowledge from SR models and incorporating it into LLMs, the recommendation effectiveness of LLMs can be improved by capturing semantic information and global context that traditional SR models may overlook .


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

The paper "DELRec: Distilling Sequential Pattern to Enhance LLM-based Recommendation" proposes a novel framework called DELRec, which introduces innovative ideas and methods to enhance the performance of Large Language Models (LLMs) in Sequential Recommendation (SR) tasks . The framework consists of two main components:

  1. SR Models Pattern Distilling: This stage focuses on extracting behavioral patterns from traditional SR models using soft prompts through well-designed strategies .
  2. LLM-based Sequential Recommendation: This component aims to fine-tune LLMs to effectively utilize the distilled auxiliary information from SR models to enhance their recommendation performance .

The DELRec framework aims to address several challenges encountered in previous approaches, such as:

  • Influence at the result level: Previous methods only influenced LLMs at the result level, limiting their effectiveness .
  • Increased complexity: Some approaches led to increased complexity in LLM recommendation methods, reducing interpretability .
  • Incomplete understanding of SR models: Previous methods had issues with incomplete understanding and utilization of information from SR models by LLMs .

To overcome these challenges, DELRec offers a new perspective by extracting knowledge from SR models to enable LLMs to better comprehend and utilize supplementary information for more effective sequential recommendations . The framework aims to improve recommendation accuracy by capturing semantic information and global context that traditional SR models may overlook . Extensive experiments conducted on real-world datasets validate the effectiveness of the DELRec framework . The DELRec framework introduces several key characteristics and advantages compared to previous methods in the field of Sequential Recommendation (SR) tasks, as outlined in the paper :

  1. Behavioral Pattern Extraction: DELRec focuses on extracting behavioral patterns from traditional SR models using soft prompts through well-designed strategies . This approach enables the framework to capture the connection between users' past interactions and changing preferences more effectively.

  2. Two Main Components: DELRec consists of two main components: SR Models Pattern Distilling and LLM-based Sequential Recommendation . This dual-stage process allows for the extraction of valuable recommendation patterns from SR models and the fine-tuning of Large Language Models (LLMs) to utilize this information for enhanced sequential recommendations.

  3. Reduced Information Loss: DELRec aims to reduce information loss and improve the recommendation effectiveness of LLMs . By distilling behavioral patterns from SR models, DELRec enhances the predictive power and adaptability of recommendation systems.

  4. Improved Recommendation Accuracy: The framework offers a new perspective for utilizing LLMs in complex SR tasks, particularly in capturing semantic information and global context that traditional SR models may overlook . This leads to improved recommendation accuracy and performance.

  5. Experimental Validation: Extensive experiments conducted on real-world datasets validate the effectiveness of the DELRec framework . The results demonstrate the framework's ability to enhance LLMs in SR tasks by leveraging knowledge extracted from SR models.

  6. Comparative Analysis: DELRec's approach of distilling recommendation behavior patterns from SR models and utilizing them in LLM-based recommendation tasks outperforms baseline methods . The framework's SR Models Pattern Distilling approach effectively extracts valuable recommendation patterns for LLMs, leading to improved performance compared to methods without this distillation process.

  7. Ablation Experiments: Ablation experiments conducted on DELRec highlight the importance of key components such as SR Models Pattern Distilling, LLMs-based Sequential Recommendation, SR Models Temporal Analysis, and Recommendation Pattern Simulating . Each component plays a crucial role in enhancing the recommendation effectiveness of LLMs by providing valuable guidance and reducing noise in the recommendation process.

Overall, DELRec's innovative framework offers a comprehensive solution to address the limitations of previous methods by leveraging the strengths of LLMs and traditional SR models to enhance sequential recommendations effectively and accurately.


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 works exist in the field of enhancing Language Model-based Sequential Recommendation (LLM-based SR) tasks. Noteworthy researchers in this area include Guohao Sun and Haoyi Zhang from Donghua University . They introduced a novel framework called DELRec, which aims to improve the performance of LLMs in SR tasks by extracting behavioral patterns from conventional SR models . The key solution proposed in the paper involves two main components: SR Models Pattern Distilling and LLM-based Sequential Recommendation. This framework not only reduces information loss but also enhances the recommendation effectiveness of LLMs by incorporating knowledge from SR models to improve sequential recommendations .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the proposed framework, DELRec, which aims to enhance the performance of Large Language Models (LLMs) in Sequential Recommendation (SR) tasks by extracting behavioral patterns from conventional SR models . The experiments consisted of two main stages:

  1. SR Models Pattern Distilling: This stage focused on extracting behavioral patterns exhibited by SR models using soft prompts through two well-designed strategies .
  2. LLMs-based Sequential Recommendation: The second stage aimed to fine-tune LLMs to effectively use the distilled auxiliary information to perform SR tasks .

The experiments involved conducting extensive experimental results on three real datasets to validate the effectiveness of the DELRec framework . Various metrics and measurement metrics were used to compare the performance of different methods and components within the framework .

Additionally, ablation studies were conducted to analyze the impact of different components of DELRec on the framework through various experiments . These ablation experiments aimed to verify the effectiveness of the SR Models Pattern Distilling and LLMs-based Sequential Recommendation stages by comparing the performance with and without specific components .


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

The dataset used for quantitative evaluation in the study is the UMass Citation Field Extraction Dataset . The code for the framework DELRec is based on FLan-T5-XXL, which is an open-source large language model .


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 verification. The paper introduces a novel framework called DELRec, which aims to enhance the performance of Large Language Models (LLMs) in Sequential Recommendation (SR) tasks by extracting behavioral patterns from conventional SR models . The experiments conducted on three real-world datasets validate the effectiveness of the DELRec framework .

The experiments address key research questions, such as whether the proposed framework outperforms baseline methods, including deep learning models and other LLM-based models for SR, and whether DELRec is able to learn meaningful recommendation behavior patterns or information . The results of these experiments demonstrate the superiority of DELRec over baseline methods and its ability to extract valuable recommendation patterns from SR models for LLMs .

Furthermore, the paper conducts ablation experiments to analyze the impact of various components within the DELRec framework . By evaluating different variants of the framework, such as with or without SR Models Pattern Distilling, LLMs-based Sequential Recommendation, SR Models Temporal Analysis, and Recommendation Pattern Simulating, the study provides a comprehensive analysis of the framework's components and their effects on performance .

Additionally, the paper explores the impact of hyperparameters, such as the size of soft prompts and the number of recommended items from the SR model, on the overall performance of DELRec . The analysis of these hyperparameters provides valuable insights into how different settings affect the framework's performance, contributing to a thorough understanding of the model's behavior and effectiveness in SR tasks.

In conclusion, the experiments and results presented in the paper offer robust support for the scientific hypotheses underlying the development and evaluation of the DELRec framework for enhancing LLM-based Sequential Recommendation tasks. The comprehensive analysis, including ablation experiments and hyperparameter studies, strengthens the validity and reliability of the proposed approach in improving recommendation systems .


What are the contributions of this paper?

The paper "DELRec: Distilling Sequential Pattern to Enhance LLM-based Recommendation" makes the following contributions:

  • Introduces the DELRec framework, which aims to enhance the performance of Large Language Models (LLMs) in Sequential Recommendation (SR) tasks by extracting behavioral patterns from conventional SR models .
  • Addresses the limitations of traditional SR models by focusing on capturing sequential patterns within training data and neglecting the broader context and semantic information embedded in item titles from external sources .
  • Proposes two main components within the DELRec framework: SR Models Pattern Distilling, which extracts behavioral patterns from SR models using soft prompts, and LLM-based Sequential Recommendation, which fine-tunes LLMs to effectively utilize the extracted auxiliary information for more accurate recommendations .
  • Conducts extensive experiments on real-world datasets to validate the effectiveness of the DELRec framework in improving recommendation effectiveness and capturing semantic information and global context that traditional SR models may miss .
  • Offers a new perspective and approach for utilizing LLMs in complex sequential recommendation tasks, providing valuable insights for future researchers in designing more efficient and accurate recommendation systems .

What work can be continued in depth?

To delve deeper into the research presented in the DELRec framework, further exploration can focus on the following aspects:

  1. Enhancing Soft Prompts Usage: Investigate the impact of utilizing soft prompts in LLM-based SR tasks, as they are not commonly employed in this context . Exploring the effectiveness of soft prompts in guiding LLMs towards more accurate recommendations could be a valuable research direction.

  2. Behavioral Pattern Distillation: Conduct a detailed analysis of the SR Models Pattern Distilling stage within the DELRec framework . This involves distilling behavioral patterns from conventional SR models to enhance LLMs' understanding and utilization of this information for improved sequential recommendations.

  3. Alignment of SR Models with LLMs: Further study the alignment of SR models with LLMs' recommendation processes, considering paradigms like LLMs Prompt with SR Text, LLMs Encoding with SR Embedding, and LLMs Prompt with SR Embedding . Exploring the effectiveness and challenges of these alignment strategies can provide insights into optimizing recommendation systems.

  4. Experimental Validation: Extend the experimental evaluation of the DELRec framework on real-world datasets . Address research questions related to the framework's performance compared to baseline methods, its ability to learn meaningful recommendation patterns, and its overall effectiveness in enhancing LLM-based sequential recommendations.

By delving deeper into these areas, researchers can advance the understanding of how to leverage behavioral patterns from SR models to empower LLMs for more effective sequential recommendations, contributing to the development of improved recommendation systems.

Tables

1

Background
LLMs in recommendation systems
Limitations of existing methods
Objective
Introducing DELRec
Enhancing LLMs for sequential recommendation
Addressing context and semantic understanding
Method
Framework: DELRec
Distillation from conventional models
Soft prompts and hard prompts
Strategies for teaching item context and semantics
Component 1: Soft Prompts
Definition and purpose
Capturing recommendation behavior
Component 2: Hard Prompts
Temporal dynamics integration
Contribution to improved understanding
Teaching Strategies
Item context learning
Semantic information extraction
Experiments
Dataset Description
Three datasets used
Evaluation
Performance metrics (accuracy)
Comparison with baseline models
Results

-DELRec's effectiveness

Parameter efficiency
Contributions
Parameter-efficient method
Improved LLM-based sequential recommendations
Enhanced item context understanding
Discussion
Advantages over existing approaches
Potential for adaptable recommendations
Future research directions
Conclusion
Summary of findings
Implications for the field
Limitations and suggestions for future work
Basic info
papers
information retrieval
artificial intelligence
Advanced features
Insights
What is the primary contribution of DELRec?
What are the two strategies employed by DELRec to enhance LLMs for recommendation?
How does DELRec address the limitations of existing sequential recommendation models?
How does DELRec perform compared to baseline models in the experiments?

DELRec: Distilling Sequential Pattern to Enhance LLM-based Recommendation

Guohao Sun, Haoyi Zhang·June 17, 2024

Summary

The paper presents DELRec, a novel framework that enhances large language models (LLMs) for sequential recommendation by distilling sequential patterns from conventional recommendation models. It addresses the limitations of existing methods by using soft prompts and two designed strategies to teach LLMs about item context and semantic information. The framework combines hard and soft prompts, with a focus on capturing recommendation behavior and temporal dynamics. Experiments on three datasets show DELRec's effectiveness, outperforming baseline models in terms of recommendation accuracy. Key contributions include a parameter-efficient method, improved LLM-based recommendations, and the demonstration of enhanced understanding of item context. The study highlights the potential of integrating sequential patterns into LLMs for more accurate and adaptable recommendations.
Mind map
Parameter efficiency
Comparison with baseline models
Performance metrics (accuracy)
Semantic information extraction
Item context learning
Contribution to improved understanding
Temporal dynamics integration
Capturing recommendation behavior
Definition and purpose
Results
Evaluation
Teaching Strategies
Component 2: Hard Prompts
Component 1: Soft Prompts
Limitations and suggestions for future work
Implications for the field
Summary of findings
Future research directions
Potential for adaptable recommendations
Advantages over existing approaches
Enhanced item context understanding
Improved LLM-based sequential recommendations
Parameter-efficient method
Dataset Description
Framework: DELRec
Conclusion
Discussion
Contributions
Experiments
Method
Outline
Background
LLMs in recommendation systems
Limitations of existing methods
Objective
Introducing DELRec
Enhancing LLMs for sequential recommendation
Addressing context and semantic understanding
Method
Framework: DELRec
Distillation from conventional models
Soft prompts and hard prompts
Strategies for teaching item context and semantics
Component 1: Soft Prompts
Definition and purpose
Capturing recommendation behavior
Component 2: Hard Prompts
Temporal dynamics integration
Contribution to improved understanding
Teaching Strategies
Item context learning
Semantic information extraction
Experiments
Dataset Description
Three datasets used
Evaluation
Performance metrics (accuracy)
Comparison with baseline models
Results

-DELRec's effectiveness

Parameter efficiency
Contributions
Parameter-efficient method
Improved LLM-based sequential recommendations
Enhanced item context understanding
Discussion
Advantages over existing approaches
Potential for adaptable recommendations
Future research directions
Conclusion
Summary of findings
Implications for the field
Limitations and suggestions for future work
Key findings
7

Paper digest

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

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide me with the title of the paper or a brief description of its topic so that I can assist you better.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that the proposed framework, DELRec, enhances the performance of Large Language Models (LLMs) in Sequential Recommendation (SR) tasks by extracting behavioral patterns from conventional SR models and enabling LLMs to effectively utilize this supplementary information for more accurate and effective sequential recommendations . The hypothesis is centered around the idea that by distilling knowledge from SR models and incorporating it into LLMs, the recommendation effectiveness of LLMs can be improved by capturing semantic information and global context that traditional SR models may overlook .


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

The paper "DELRec: Distilling Sequential Pattern to Enhance LLM-based Recommendation" proposes a novel framework called DELRec, which introduces innovative ideas and methods to enhance the performance of Large Language Models (LLMs) in Sequential Recommendation (SR) tasks . The framework consists of two main components:

  1. SR Models Pattern Distilling: This stage focuses on extracting behavioral patterns from traditional SR models using soft prompts through well-designed strategies .
  2. LLM-based Sequential Recommendation: This component aims to fine-tune LLMs to effectively utilize the distilled auxiliary information from SR models to enhance their recommendation performance .

The DELRec framework aims to address several challenges encountered in previous approaches, such as:

  • Influence at the result level: Previous methods only influenced LLMs at the result level, limiting their effectiveness .
  • Increased complexity: Some approaches led to increased complexity in LLM recommendation methods, reducing interpretability .
  • Incomplete understanding of SR models: Previous methods had issues with incomplete understanding and utilization of information from SR models by LLMs .

To overcome these challenges, DELRec offers a new perspective by extracting knowledge from SR models to enable LLMs to better comprehend and utilize supplementary information for more effective sequential recommendations . The framework aims to improve recommendation accuracy by capturing semantic information and global context that traditional SR models may overlook . Extensive experiments conducted on real-world datasets validate the effectiveness of the DELRec framework . The DELRec framework introduces several key characteristics and advantages compared to previous methods in the field of Sequential Recommendation (SR) tasks, as outlined in the paper :

  1. Behavioral Pattern Extraction: DELRec focuses on extracting behavioral patterns from traditional SR models using soft prompts through well-designed strategies . This approach enables the framework to capture the connection between users' past interactions and changing preferences more effectively.

  2. Two Main Components: DELRec consists of two main components: SR Models Pattern Distilling and LLM-based Sequential Recommendation . This dual-stage process allows for the extraction of valuable recommendation patterns from SR models and the fine-tuning of Large Language Models (LLMs) to utilize this information for enhanced sequential recommendations.

  3. Reduced Information Loss: DELRec aims to reduce information loss and improve the recommendation effectiveness of LLMs . By distilling behavioral patterns from SR models, DELRec enhances the predictive power and adaptability of recommendation systems.

  4. Improved Recommendation Accuracy: The framework offers a new perspective for utilizing LLMs in complex SR tasks, particularly in capturing semantic information and global context that traditional SR models may overlook . This leads to improved recommendation accuracy and performance.

  5. Experimental Validation: Extensive experiments conducted on real-world datasets validate the effectiveness of the DELRec framework . The results demonstrate the framework's ability to enhance LLMs in SR tasks by leveraging knowledge extracted from SR models.

  6. Comparative Analysis: DELRec's approach of distilling recommendation behavior patterns from SR models and utilizing them in LLM-based recommendation tasks outperforms baseline methods . The framework's SR Models Pattern Distilling approach effectively extracts valuable recommendation patterns for LLMs, leading to improved performance compared to methods without this distillation process.

  7. Ablation Experiments: Ablation experiments conducted on DELRec highlight the importance of key components such as SR Models Pattern Distilling, LLMs-based Sequential Recommendation, SR Models Temporal Analysis, and Recommendation Pattern Simulating . Each component plays a crucial role in enhancing the recommendation effectiveness of LLMs by providing valuable guidance and reducing noise in the recommendation process.

Overall, DELRec's innovative framework offers a comprehensive solution to address the limitations of previous methods by leveraging the strengths of LLMs and traditional SR models to enhance sequential recommendations effectively and accurately.


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 works exist in the field of enhancing Language Model-based Sequential Recommendation (LLM-based SR) tasks. Noteworthy researchers in this area include Guohao Sun and Haoyi Zhang from Donghua University . They introduced a novel framework called DELRec, which aims to improve the performance of LLMs in SR tasks by extracting behavioral patterns from conventional SR models . The key solution proposed in the paper involves two main components: SR Models Pattern Distilling and LLM-based Sequential Recommendation. This framework not only reduces information loss but also enhances the recommendation effectiveness of LLMs by incorporating knowledge from SR models to improve sequential recommendations .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the proposed framework, DELRec, which aims to enhance the performance of Large Language Models (LLMs) in Sequential Recommendation (SR) tasks by extracting behavioral patterns from conventional SR models . The experiments consisted of two main stages:

  1. SR Models Pattern Distilling: This stage focused on extracting behavioral patterns exhibited by SR models using soft prompts through two well-designed strategies .
  2. LLMs-based Sequential Recommendation: The second stage aimed to fine-tune LLMs to effectively use the distilled auxiliary information to perform SR tasks .

The experiments involved conducting extensive experimental results on three real datasets to validate the effectiveness of the DELRec framework . Various metrics and measurement metrics were used to compare the performance of different methods and components within the framework .

Additionally, ablation studies were conducted to analyze the impact of different components of DELRec on the framework through various experiments . These ablation experiments aimed to verify the effectiveness of the SR Models Pattern Distilling and LLMs-based Sequential Recommendation stages by comparing the performance with and without specific components .


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

The dataset used for quantitative evaluation in the study is the UMass Citation Field Extraction Dataset . The code for the framework DELRec is based on FLan-T5-XXL, which is an open-source large language model .


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 verification. The paper introduces a novel framework called DELRec, which aims to enhance the performance of Large Language Models (LLMs) in Sequential Recommendation (SR) tasks by extracting behavioral patterns from conventional SR models . The experiments conducted on three real-world datasets validate the effectiveness of the DELRec framework .

The experiments address key research questions, such as whether the proposed framework outperforms baseline methods, including deep learning models and other LLM-based models for SR, and whether DELRec is able to learn meaningful recommendation behavior patterns or information . The results of these experiments demonstrate the superiority of DELRec over baseline methods and its ability to extract valuable recommendation patterns from SR models for LLMs .

Furthermore, the paper conducts ablation experiments to analyze the impact of various components within the DELRec framework . By evaluating different variants of the framework, such as with or without SR Models Pattern Distilling, LLMs-based Sequential Recommendation, SR Models Temporal Analysis, and Recommendation Pattern Simulating, the study provides a comprehensive analysis of the framework's components and their effects on performance .

Additionally, the paper explores the impact of hyperparameters, such as the size of soft prompts and the number of recommended items from the SR model, on the overall performance of DELRec . The analysis of these hyperparameters provides valuable insights into how different settings affect the framework's performance, contributing to a thorough understanding of the model's behavior and effectiveness in SR tasks.

In conclusion, the experiments and results presented in the paper offer robust support for the scientific hypotheses underlying the development and evaluation of the DELRec framework for enhancing LLM-based Sequential Recommendation tasks. The comprehensive analysis, including ablation experiments and hyperparameter studies, strengthens the validity and reliability of the proposed approach in improving recommendation systems .


What are the contributions of this paper?

The paper "DELRec: Distilling Sequential Pattern to Enhance LLM-based Recommendation" makes the following contributions:

  • Introduces the DELRec framework, which aims to enhance the performance of Large Language Models (LLMs) in Sequential Recommendation (SR) tasks by extracting behavioral patterns from conventional SR models .
  • Addresses the limitations of traditional SR models by focusing on capturing sequential patterns within training data and neglecting the broader context and semantic information embedded in item titles from external sources .
  • Proposes two main components within the DELRec framework: SR Models Pattern Distilling, which extracts behavioral patterns from SR models using soft prompts, and LLM-based Sequential Recommendation, which fine-tunes LLMs to effectively utilize the extracted auxiliary information for more accurate recommendations .
  • Conducts extensive experiments on real-world datasets to validate the effectiveness of the DELRec framework in improving recommendation effectiveness and capturing semantic information and global context that traditional SR models may miss .
  • Offers a new perspective and approach for utilizing LLMs in complex sequential recommendation tasks, providing valuable insights for future researchers in designing more efficient and accurate recommendation systems .

What work can be continued in depth?

To delve deeper into the research presented in the DELRec framework, further exploration can focus on the following aspects:

  1. Enhancing Soft Prompts Usage: Investigate the impact of utilizing soft prompts in LLM-based SR tasks, as they are not commonly employed in this context . Exploring the effectiveness of soft prompts in guiding LLMs towards more accurate recommendations could be a valuable research direction.

  2. Behavioral Pattern Distillation: Conduct a detailed analysis of the SR Models Pattern Distilling stage within the DELRec framework . This involves distilling behavioral patterns from conventional SR models to enhance LLMs' understanding and utilization of this information for improved sequential recommendations.

  3. Alignment of SR Models with LLMs: Further study the alignment of SR models with LLMs' recommendation processes, considering paradigms like LLMs Prompt with SR Text, LLMs Encoding with SR Embedding, and LLMs Prompt with SR Embedding . Exploring the effectiveness and challenges of these alignment strategies can provide insights into optimizing recommendation systems.

  4. Experimental Validation: Extend the experimental evaluation of the DELRec framework on real-world datasets . Address research questions related to the framework's performance compared to baseline methods, its ability to learn meaningful recommendation patterns, and its overall effectiveness in enhancing LLM-based sequential recommendations.

By delving deeper into these areas, researchers can advance the understanding of how to leverage behavioral patterns from SR models to empower LLMs for more effective sequential recommendations, contributing to the development of improved recommendation systems.

Tables
1
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