arXiv cs.AI· Ziwei Liu, Yejing Wang, Shengyu Zhou, Xinhang Li, Xiangyu Zhao·· 14 小时前AI 评分22
ComeIR：面向生成式推荐的条件记忆增强物品表示框架
Conditional Memory Enhanced Item Representation for Generative Recommendation
AI 导读
针对生成式推荐中 SID 表示阶段物品语义丢失与结构化 SID 关系建模不足的问题，研究者提出 ComeIR 框架，将 SID code 嵌入重建为物品感知输入并保留结构化 SID 证据。该框架包含物品引导的 Token Scoring、双层条件记忆与 Memory Merge，以及记忆恢复预测头。实验显示 ComeIR 有效且灵活，扩大条件记忆还能带来可扩展的收益。
正文 · 原文
Abstract:Generative recommendation (GR) has emerged as a promising paradigm that predicts target items by autoregressively generating their semantic identifiers (SID). Most GR methods follow a quantization-representation-generation pipeline. While most research focuses on quantization and generation, the representation that bridges the two remains underexplored. Existing representation constructions mainly take two forms: either directly flattening the SID code embeddings into a sequence or merging them into a compact vector via external networks. While effective, they still expose two practical challenges: \textbf{1) Loss of item semantics:} Information lost by quantization or compression needs to be recovered during the representation stage. \textbf{2) Insufficient modeling of structured SID relations:} Intra-item code composition patterns and inter-item prefix-transition patterns are not explicitly preserved for representation and subsequent SID generation.
To overcome these challenges, we propose \textbf{ComeIR}, a \textbf{\uline{Co}}nditional \textbf{\uline{me}}mory Enhanced \textbf{\uline{I}}tem \textbf{\uline{R}}epresentation framework that reconstructs SID code embeddings into item-aware inputs and preserves structured SID evidence for prediction. Item-guided Token Scoring estimates the contribution of each SID code using the item embedding employed by the quantizer. Dual-level Conditional Memory captures intra-item prefix composition and inter-item prefix transitions, while Memory Merge integrates these signals into each historical item representation. The Memory-restoring Prediction Head further conditions SID-level prediction on the generated prefix and historical transition evidence. Extensive experiments demonstrate the effectiveness and flexibility of ComeIR, and further reveal scalable gains from enlarging conditional memory.
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.11447 [cs.IR] |
| (or arXiv:2605.11447v2 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.11447 arXiv-issued DOI via DataCite |
Submission history
From: Ziwei Liu [view email]
[v1]
Tue, 12 May 2026 02:56:59 UTC (538 KB)
[v2]
Wed, 28 Oct 2026 09:28:27 UTC (1,037 KB)
来源：arXiv cs.AI · arxiv.org