Conditional Memory Enhanced Item Representation for Generative Recommendation

• URL: http://arxiv.org/abs/2605.11447v1
• Date: Tue, 12 May 2026 02:56:59 GMT
• Title: Conditional Memory Enhanced Item Representation for Generative Recommendation
• Abstract summary: Generative recommendation (GR) has emerged as a promising paradigm that predicts target items by autoregressively generating their semantic identifiers (SID)<n>Most GR methods follow a quantization-representation-generation pipeline, first assigning each item a SID, then constructing input representations from SID-token embeddings, and finally predicting the target SID through autoregressive generation.<n>We propose ComeIR, a Conditional Memory enhanced Item Representation framework that reconstructs SID-token embeddings into item-aware inputs and restores the token granularity during SID decoding.
• Score: 46.59623438612085
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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, first assigning each item a SID, then constructing input representations from SID-token embeddings, and finally predicting the target SID through autoregressive generation. Existing item-level representation constructions mainly take two forms: directly merging SID-token embeddings into a compact vector, or enriching item-level representations with external inputs through additional networks. However, these item-level constructors still expose two practical challenges: direct merging may amplify the information loss caused by quantization and ID collision while obscuring SID code relations, whereas external-input-based methods can strengthen item semantics but cannot reliably preserve the SID-structured evidence required for token-level generation. These limitations make representation construction an underexplored bottleneck, leading to two severe problems, \ie{} the Identity-Structure Preservation Conflict and Input-Output Granularity Mismatch. To this end, we propose ComeIR, a Conditional Memory enhanced Item Representation framework that reconstructs SID-token embeddings into item-aware inputs and restores the token granularity during SID decoding. Specifically, MM-guided token scoring adaptively estimates the contribution of each code within the SID, dual-level Engram memory captures intra-item code composition and inter-item transition patterns, and a memory-restoring prediction head reuses the memories during SID decoding. Extensive experiments demonstrate the effectiveness and flexibility of ComeIR, and further reveal scalable gains from enlarging conditional memory.

Related papers

• Rethinking Item Tokenization in Generative Recommenders: From Fixed Atoms to Semantic Subwords [50.621722853036005]
  In generative recommender systems, items are typically tokenized into fixed-length semantic ID sequences for autoregressive next-item prediction.<n>For user-context modeling, this fine-grained representation triggers Intra-item Attention Overload.<n>We propose Semantic Subword Tokenization (SST), which represents historical items as variable-length semantic subwords.
  arXiv  Detail & Related papers  (2026-08-24T02:42:51Z)
• Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation [28.83516611687355]
  We propose Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation (LGRID)<n>LGRID introduces a generative disentanglement paradigm through an Encode -> Disentangle -> Align -> Quantize pipeline.<n> Experiments on Kuaishou and Foursquare show that LGRID consistently outperforms strong SID baselines.
  arXiv  Detail & Related papers  (2026-07-30T09:52:18Z)
• TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation [25.345503799250476]
  TopoGR is a topology-preserving generative recommendation framework based on Bit-decomposable Semantic ID( Binary SID)<n>We show that TopoGR consistently outperforms existing state-of-the-art baselines in recommendation performance.
  arXiv  Detail & Related papers  (2026-07-28T02:45:33Z)
• CogRec: Structure-Cognitive Fast-and-Slow Reasoning for Generative Recommendation [29.663400191042996]
  CogRec is a structure-cognitive fast-and-slow reasoning framework.<n>CogRec augments the vertical SID hierarchy with intra-layer semantic graphs and item-level neighborhoods.<n> SID Routing represents recommendation reasoning through layer-wise Match, LateralJump, and Explore operations.
  arXiv  Detail & Related papers  (2026-07-27T13:19:55Z)
• CARD: Non-Uniform Quantization of Visual Semantic Unit for Generative Recommendation [16.01204624133875]
  We propose a novel generative recommendation framework, called CARD.<n> CARD introduces a visual semantic unit that unifies textual, visual, and collaborative signals into a structured visual representation prior to encoding.<n>We develop a non-uniform quantization framework, which incorporates a learnable and invertible non-uniform transformation into the quantization process to map skewed semantic distributions into a more balanced latent space.
  arXiv  Detail & Related papers  (2026-04-29T08:38:14Z)
• UniRec: Bridging the Expressive Gap between Generative and Discriminative Recommendation via Chain-of-Attribute [12.89120699793625]
  Generative Recommendation (GR) reframes retrieval and ranking as autoregressive decoding over Semantic IDs (SIDs)<n>We formalize this via Bayes' theorem: ranking by p(y|f,u) is equivalent to ranking by p(f|y,u), which factorizes autoregressively over item features.<n>We propose UniRec with Chain-of-Attribute (CoA) as its core mechanism. CoA prefixes each SID sequence with structured attribute tokens:category, seller, brand, before decoding the SID, recovering the item-side feature crossing that discriminative models exploit.
  arXiv  Detail & Related papers  (2026-04-14T03:13:50Z)
• Reasoning over Semantic IDs Enhances Generative Recommendation [56.83043583765322]
  We propose a two-stage framework that elicits reasoning over SIDs by strengthening SID--language alignment.<n> SIDReasoner further improves recommendation reasoning through outcome-driven reinforced optimization.<n>Our experiments on three real-world datasets demonstrate the effectiveness of our reasoning-augmented SID-based generative recommendation.
  arXiv  Detail & Related papers  (2026-03-24T13:31:48Z)
• IntRR: A Framework for Integrating SID Redistribution and Length Reduction [14.327886721362647]
  We propose IntRR, a novel framework that integrates objective-aligned SID Redistribution and structural Length Reduction.<n>IntRR yields substantial improvements over representative generative baselines, achieving superior performance in both recommendation accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-02-24T09:09:40Z)
• End-to-End Semantic ID Generation for Generative Advertisement Recommendation [33.453121305193434]
  We propose a Unified SID generation framework for generative advertisement recommendation.<n>Specifically, we jointly optimize embeddings and SIDs in an end-to-end manner from raw advertising data.<n>Experiments demonstrate that UniSID consistently outperforms state-of-the-art SID generation methods.
  arXiv  Detail & Related papers  (2026-02-11T02:38:26Z)
• The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation [51.62815306481903]
  We propose textbfname, a novel framework that harmonizes the SID and HID. Specifically, we devise a dual-branch modeling architecture that enables the model to capture both the multi-granular semantics within SID while preserving the unique collaborative identity of HID.<n>Experiments on three real-world datasets show that name balances recommendation quality for both head and tail items while surpassing the existing baselines.
  arXiv  Detail & Related papers  (2025-12-11T07:50:53Z)
• DiffGRM: Diffusion-based Generative Recommendation Model [63.35379395455103]
  Generative recommendation (GR) is an emerging paradigm that represents each item via a tokenizer as an n-digit semantic ID (SID)<n>We propose DiffGRM, a diffusion-based GR model that replaces the autoregressive decoder with a masked discrete diffusion model (MDM)<n> Experiments show consistent gains over strong generative and discriminative recommendation baselines on multiple datasets.
  arXiv  Detail & Related papers  (2025-10-21T03:23:32Z)
• Order-agnostic Identifier for Large Language Model-based Generative Recommendation [94.37662915542603]
  Items are assigned identifiers for Large Language Models (LLMs) to encode user history and generate the next item.<n>Existing approaches leverage either token-sequence identifiers, representing items as discrete token sequences, or single-token identifiers, using ID or semantic embeddings.<n>We propose SETRec, which leverages semantic tokenizers to obtain order-agnostic multi-dimensional tokens.
  arXiv  Detail & Related papers  (2025-02-15T15:25:38Z)
• UGMAE: A Unified Framework for Graph Masked Autoencoders [67.75493040186859]
  We propose UGMAE, a unified framework for graph masked autoencoders. We first develop an adaptive feature mask generator to account for the unique significance of nodes. We then design a ranking-based structure reconstruction objective joint with feature reconstruction to capture holistic graph information.
  arXiv  Detail & Related papers  (2024-02-12T19:39:26Z)

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.