Large Language Models Enhanced Sequential Recommendation for Long-tail User and Item

• URL: http://arxiv.org/abs/2405.20646v1
• Date: Fri, 31 May 2024 07:24:42 GMT
• Title: Large Language Models Enhanced Sequential Recommendation for Long-tail User and Item
• Authors: Qidong Liu, Xian Wu, Xiangyu Zhao, Yejing Wang, Zijian Zhang, Feng Tian, Yefeng Zheng,
• Abstract summary: The emergence of large language models (LLMs) presents a promising avenue to address these challenges from a semantic standpoint. In this study, we introduce the Large Language Models Enhancement framework for Sequential Recommendation (LLM-ESR) Our proposed enhancement framework demonstrates superior performance compared to existing methodologies.
• Score: 58.04939553630209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential recommendation systems (SRS) serve the purpose of predicting users' subsequent preferences based on their past interactions and have been applied across various domains such as e-commerce and social networking platforms. However, practical SRS encounters challenges due to the fact that most users engage with only a limited number of items, while the majority of items are seldom consumed. These challenges, termed as the long-tail user and long-tail item dilemmas, often create obstacles for traditional SRS methods. Mitigating these challenges is crucial as they can significantly impact user satisfaction and business profitability. While some research endeavors have alleviated these issues, they still grapple with issues such as seesaw or noise stemming from the scarcity of interactions. The emergence of large language models (LLMs) presents a promising avenue to address these challenges from a semantic standpoint. In this study, we introduce the Large Language Models Enhancement framework for Sequential Recommendation (LLM-ESR), which leverages semantic embeddings from LLMs to enhance SRS performance without increasing computational overhead. To combat the long-tail item challenge, we propose a dual-view modeling approach that fuses semantic information from LLMs with collaborative signals from traditional SRS. To address the long-tail user challenge, we introduce a retrieval augmented self-distillation technique to refine user preference representations by incorporating richer interaction data from similar users. Through comprehensive experiments conducted on three authentic datasets using three widely used SRS models, our proposed enhancement framework demonstrates superior performance compared to existing methodologies.

Related papers

• LLM-Driven Dual-Level Multi-Interest Modeling for Recommendation [12.89199121698673]
  Large language models (LLMs) show significant potential for multi-interest analysis due to their extensive knowledge and powerful reasoning capabilities.<n>We propose an LLM-driven dual-level multi-interest modeling framework for more effective recommendation.<n> Experiments on real-world datasets show the superiority of our approach against state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-15T02:13:54Z)
• Multi-agents based User Values Mining for Recommendation [52.26100802380767]
  We propose a zero-shot multi-LLM collaborative framework for effective and accurate user value extraction.<n>We apply text summarization techniques to condense item content while preserving essential meaning.<n>To mitigate hallucinations, we introduce two specialized agent roles: evaluators and supervisors.
  arXiv  Detail & Related papers  (2025-05-02T04:01:31Z)
• LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation [9.190184562550384]
  Sequential Recommender Systems (SRS) have become a cornerstone of online platforms, leveraging users' historical interaction data to forecast their next potential engagement. Current methodologies encounter obstacles, including the absence of collaborative signals and the prevalence of hallucination phenomena. We present LLMSeR, an innovative framework that utilizes Large Language Models (LLMs) to generate pseudo-prior items.
  arXiv  Detail & Related papers  (2025-03-16T15:32:30Z)
• LIBER: Lifelong User Behavior Modeling Based on Large Language Models [42.045535303737694]
  We propose Lifelong User Behavior Modeling (LIBER) based on large language models. LIBER has been deployed on Huawei's music recommendation service and achieved substantial improvements in users' play count and play time by 3.01% and 7.69%.
  arXiv  Detail & Related papers  (2024-11-22T03:43:41Z)
• LLM-assisted Explicit and Implicit Multi-interest Learning Framework for Sequential Recommendation [50.98046887582194]
  We propose an explicit and implicit multi-interest learning framework to model user interests on two levels: behavior and semantics. The proposed EIMF framework effectively and efficiently combines small models with LLM to improve the accuracy of multi-interest modeling.
  arXiv  Detail & Related papers  (2024-11-14T13:00:23Z)
• 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)
• Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation [83.87767101732351]
  Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. We propose DARec, a sequential recommendation model built on top of coarse-grained adaption for capturing inter-item relations.
  arXiv  Detail & Related papers  (2024-08-14T10:03:40Z)
• Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation [50.837277466987345]
  We focus on the field of large language models (LLMs) for recommendation. We propose RecLoRA, which incorporates a Personalized LoRA module that maintains independent LoRAs for different users. We also design a Few2Many Learning Strategy, using a conventional recommendation model as a lens to magnify small training spaces to full spaces.
  arXiv  Detail & Related papers  (2024-08-07T04:20:28Z)
• GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation [13.1192216083304]
  We propose a novel Graphs and User Modalities Enhancement (GUME) for long-tail multimodal recommendation. Specifically, we first enhance the user-item graph using multimodal similarity between items. We then construct two types of user modalities: explicit interaction features and extended interest features.
  arXiv  Detail & Related papers  (2024-07-17T06:29:00Z)
• MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation [8.751117923894435]
  The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) We propose a novel framework for SRS, called Mutual Enhancement of Long-Tailed user and item (MELT) MELT jointly alleviates the long-tailed problem in the perspectives of both users and items.
  arXiv  Detail & Related papers  (2023-04-17T15:49:34Z)
• Sequential Search with Off-Policy Reinforcement Learning [48.88165680363482]
  We propose a highly scalable hybrid learning model that consists of an RNN learning framework and an attention model. As a novel optimization step, we fit multiple short user sequences in a single RNN pass within a training batch, by solving a greedy knapsack problem on the fly. We also explore the use of off-policy reinforcement learning in multi-session personalized search ranking.
  arXiv  Detail & Related papers  (2022-02-01T06:52:40Z)

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.