DELRec: Distilling Sequential Pattern to Enhance LLM-based Recommendation

• URL: http://arxiv.org/abs/2406.11156v2
• Date: Tue, 18 Jun 2024 04:00:59 GMT
• Title: DELRec: Distilling Sequential Pattern to Enhance LLM-based Recommendation
• Authors: Guohao Sun, Haoyi Zhang,
• Abstract summary: Sequential recommendation (SR) tasks enhance recommendation accuracy by capturing the connection between users' past interactions and their changing preferences. Conventional models often focus solely on capturing sequential patterns within the training data, neglecting the broader context and semantic information embedded in item titles from external sources. DelRec aims to extract knowledge from SR models and enable LLMs to easily comprehend and utilize this supplementary information for more effective sequential recommendations.
• Score: 3.5113201254928117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential recommendation (SR) tasks enhance recommendation accuracy by capturing the connection between users' past interactions and their changing preferences. Conventional models often focus solely on capturing sequential patterns within the training data, neglecting the broader context and semantic information embedded in item titles from external sources. This limits their predictive power and adaptability. Recently, large language models (LLMs) have shown promise in SR tasks due to their advanced understanding capabilities and strong generalization abilities. Researchers have attempted to enhance LLMs' recommendation performance by incorporating information from SR models. However, previous approaches have encountered problems such as 1) only influencing LLMs at the result level; 2) increased complexity of LLMs recommendation methods leading to reduced interpretability; 3) incomplete understanding and utilization of SR models information by LLMs. To address these problems, we proposes a novel framework, DELRec, which aims to extract knowledge from SR models and enable LLMs to easily comprehend and utilize this supplementary information for more effective sequential recommendations. DELRec consists of two main stages: 1) SR Models Pattern Distilling, focusing on extracting behavioral patterns exhibited by SR models using soft prompts through two well-designed strategies; 2) LLMs-based Sequential Recommendation, aiming to fine-tune LLMs to effectively use the distilled auxiliary information to perform SR tasks. Extensive experimental results conducted on three real datasets validate the effectiveness of the DELRec framework.

Related papers

• Enhancing High-order Interaction Awareness in LLM-based Recommender Model [3.7623606729515133]
  This paper presents an enhanced LLM-based recommender (ELMRec) We enhance whole-word embeddings to substantially enhance LLMs' interpretation of graph-constructed interactions for recommendations. Our ELMRec outperforms state-of-the-art (SOTA) methods in both direct and sequential recommendations.
  arXiv  Detail & Related papers  (2024-09-30T06:07:12Z)
• Large Language Model Empowered Embedding Generator for Sequential Recommendation [57.49045064294086]
  Large Language Model (LLM) has the potential to understand the semantic connections between items, regardless of their popularity. We present LLMEmb, an innovative technique that harnesses LLM to create item embeddings that bolster the performance of Sequential Recommender Systems.
  arXiv  Detail & Related papers  (2024-09-30T03:59:06Z)
• SLMRec: Empowering Small Language Models for Sequential Recommendation [38.51895517016953]
  Sequential Recommendation task involves predicting the next item a user is likely to interact with, given their past interactions. Recent research demonstrates the great impact of LLMs on sequential recommendation systems. Due to the huge size of LLMs, it is inefficient and impractical to apply a LLM-based model in real-world platforms.
  arXiv  Detail & Related papers  (2024-05-28T07:12:06Z)
• In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery [5.2387832710686695]
  In this work, we introduce the first comprehensive framework that utilizes Large Language Models (LLMs) for the task of Symbolic Regression. We propose In-Context Symbolic Regression (ICSR), an SR method which iteratively refines a functional form with an external LLM and determines its coefficients with an external LLM. Our findings reveal that LLMs are able to successfully find symbolic equations that fit the given data, matching or outperforming the overall performance of the best SR baselines on four popular benchmarks.
  arXiv  Detail & Related papers  (2024-04-29T20:19:25Z)
• Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation [23.182787000804407]
  Large Language Models (LLMs) are emerging as promising approaches to enhance session-based recommendation (SBR) We propose a Reflective Reinforcement Large Language Model (Re2LLM) for SBR, guiding LLMs to focus on specialized knowledge essential for more accurate recommendations.
  arXiv  Detail & Related papers  (2024-03-25T05:12:18Z)
• Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation [128.01050030936028]
  We propose an information refinement training method named InFO-RAG. InFO-RAG is low-cost and general across various tasks. It improves the performance of LLaMA2 by an average of 9.39% relative points.
  arXiv  Detail & Related papers  (2024-02-28T08:24:38Z)
• Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning [79.32236399694077]
  Low-quality data in the training set are usually detrimental to instruction tuning. We propose a novel method, termed "reflection-tuning" This approach utilizes an oracle LLM to recycle the original training data by introspecting and enhancing the quality of instructions and responses in the data.
  arXiv  Detail & Related papers  (2023-10-18T05:13:47Z)
• TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models [52.734140807634624]
  Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs. We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
  arXiv  Detail & Related papers  (2023-10-10T16:38:49Z)
• LLMRec: Benchmarking Large Language Models on Recommendation Task [54.48899723591296]
  The application of Large Language Models (LLMs) in the recommendation domain has not been thoroughly investigated. We benchmark several popular off-the-shelf LLMs on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization. The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
  arXiv  Detail & Related papers  (2023-08-23T16:32:54Z)
• ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation [43.270424225285105]
  We focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. We propose Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings.
  arXiv  Detail & Related papers  (2023-08-22T02:25:04Z)
• On Learning to Summarize with Large Language Models as References [101.79795027550959]
  Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets. We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
  arXiv  Detail & Related papers  (2023-05-23T16:56:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.