大模型
arxiv.org ·
通过协调双动态索引机制释放大语言模型在序列推荐中的潜力
该论文提出 ED²（End-to-End Dual Dynamic）推荐器，称其为首个采用双动态索引机制的 LLM 序列推荐系统，旨在解决现有 LLM 序列推荐中索引生成与推荐过程割裂、语义信息与协同信息融合不足，以及忽视用户相关信息导致难以利用高阶用户-物品交互模式的问题。方法上，双动态索引机制将索引生成与序列推荐整合进统一的 LLM 主干流水线，并支持引入用户相关信息；同时提出多粒度 token 调节器，基于 LLM 语义知识在多种表示粒度上构建对齐监督，并定制用户集合数据与指令微调任务以捕捉高阶交互模式。在三个公开数据集上的实验显示，Hit-Rate 平均提升 19.62%，NDCG 平均提升 21.11%。
AI推荐理由 arXiv 论文（v2 修订版），提出 ED² 推荐器，将索引生成与序列推荐统一到 LLM 主干流水线，并引入用户相关信息与多粒度 token 调节器；在三个公开数据集上报告 Hit-Rate 平均提升 19.62%、NDCG 提升 21.11%。属可验证的学术成果，但摘要未给出基线细节与消融，实际效果仍需复现验证；主题偏推荐系统，对通用 AI/Agent 读者的直接价值有限。
72
正文
Computer Science > Information Retrieval
arXiv:2409.09253 (cs)
[Submitted on 14 Sep 2024 (v1), last revised 16 Sep 2026 (this version, v2)]
Title:Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanism
Authors:Jun Yin, Zhengxin Zeng, Mingzheng Li, Hao Yan, Chaozhuo Li, Weihao Han, Jianjin Zhang, Ruochen Liu, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang, Shirui Pan, Senzhang Wang
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Abstract:Owing to the unprecedented capability in semantic understanding and logical reasoning, large language models (LLMs) have shown fantastic potential in developing next-generation sequential recommender systems (RSs). However, existing LLM-based sequential RSs mostly separate index generation from sequential recommendation, leading to insufficient integration between semantic information and collaborative information. On the other hand, the neglect of user-related information hinders LLM-based sequential RSs from exploiting high-order user-item interaction patterns. In this paper, we propose the End-to-End Dual Dynamic (ED$^2$) recommender, the first LLM-based sequential RS which adopts dual dynamic index mechanism, targeting resolving the above limitations simultaneously. The dual dynamic index mechanism can not only assembly index generation and sequential recommendation into a unified LLM-backbone pipeline, but also make it practical for LLM-based sequential recommender to take advantage of user-related information. Specifically, to facilitate the LLM comprehension ability to dual dynamic index, we propose a multigrained token regulator which constructs alignment supervision based on LLMs semantic knowledge across multiple representation granularities. Moreover, the associated user collection data and a series of novel instruction tuning tasks are specially customized to capture the high-order user-item interaction patterns. Extensive experiments on three public datasets demonstrate the superiority of ED$^2$, achieving an average improvement of 19.62% in Hit-Rate and 21.11% in NDCG.
Subjects:
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:
arXiv:2409.09253 [cs.IR]
(or
arXiv:2409.09253v2 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2409.09253
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arXiv-issued DOI via DataCite
Submission history
From: Jun Yin [view email]
[v1]
Sat, 14 Sep 2024 01:45:04 UTC (1,965 KB)
[v2]
Wed, 16 Sep 2026 16:42:42 UTC (768 KB)
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