LoopMemGR: From Behavior Logs to Evolving Memory for Generative Recommendation

• URL: http://arxiv.org/abs/2607.27647v1
• Date: Thu, 30 Jul 2026 03:59:00 GMT
• Title: LoopMemGR: From Behavior Logs to Evolving Memory for Generative Recommendation
• Abstract summary: Generative recommendation formulates next-item prediction as conditional autoregressive generation over discrete Semantic IDs.<n>We propose LoopMemGR, a closed-loop recommendation experience memory framework for generative recommendation.
• Score: 14.05042925405816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative recommendation formulates next-item prediction as conditional autoregressive generation over discrete Semantic IDs, enabling end-to-end recommendation over large-scale item spaces. However, most existing methods follow a history-as-context paradigm that repeatedly reconstructs user preference from behavior history while discarding system-side recommendation decisions after each request. This creates an asymmetric memory: the system remembers what the user has done, but not what it has previously recommended or learned from the resulting feedback. Consequently, useful preference-validation signals, potential negative evidence, and historical exploration information cannot be directly reused across requests. To address these limitations, we propose LoopMemGR, a closed-loop recommendation experience memory framework for generative recommendation. In addition to the conventional behavior log, LoopMemGR maintains a recommendation experience log that records past recommendation--feedback trajectories. It extracts request-relevant evidence through three complementary views: the recency view captures short-term interaction dynamics, the frequency view summarizes recurring recommendation patterns, and the global view distills transferable regularities shared across users. These signals are compressed into a fixed number of experience tokens to condition the generative backbone under a bounded input budget. Extensive experiments on an industrial Taobao dataset demonstrate the effectiveness of closed-loop experience accumulation and multi-view experience extraction.

Related papers

• RecRec: Latent Interests Recursive Reasoning for Sequential Recommendation [52.23179023639867]
  Sequential recommender systems rely on a single forward pass to encode user interaction and predict the next item.<n>How to structure the reasoning process for sequential recommendation remains an open question.<n>Existing approaches couple reasoning and prediction in a single $d$-dimensional state, limiting reasoning depth.<n>We propose RecRec (Recursive Reasoning for Recommendation), an RL-free framework that overcomes fixed $d$-dimensional state of methods.
  arXiv  Detail & Related papers  (2026-07-14T16:28:28Z)
• RecRec: Recursive Refinement for Sequential Recommendation [2.134948383299948]
  Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement.<n>We propose RecRec, a lightweight model that maintains a compact latent state and updates it through a shared module conditioned on interaction evidence.<n> Experiments show that RecRec matches or outperforms state-of-the-art sequential, graph-based, and reasoning-enhanced recommenders while using only 3.9M to 14M parameters.
  arXiv  Detail & Related papers  (2026-07-12T02:53:17Z)
• RAGR: Review-Augmented Generative Recommendation [61.29879991713178]
  Review-Augmented Generative Recommendation (RAGR) is a novel GR framework that incorporates review feedback directly into the generative user sequence.<n>RAGR yields consistent and significant gains over strong GR backbones across all metrics.
  arXiv  Detail & Related papers  (2026-05-17T05:21:23Z)
• HyMiRec: A Hybrid Multi-interest Learning Framework for LLM-based Sequential Recommendation [24.720767926024433]
  HyMiRec is a hybrid sequential recommendation framework for large language models.<n>It extracts coarse interest embeddings from long user sequences and an LLM-based recommender to captures refined interest embeddings.<n>To model the diverse preferences of users, we design a disentangled multi-interest learning module.
  arXiv  Detail & Related papers  (2025-10-15T16:45:59Z)
• Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure [75.45798019935947]
  We propose counterfactual augmentation over system exposure for sequential recommendation (CaseRec)<n>CaseRec introduces reinforcement learning to account for different exposure rewards.<n>A transformer-based user simulator is proposed to predict the user feedback reward for the augmented items.
  arXiv  Detail & Related papers  (2025-04-18T05:46:27Z)
• Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation [23.546871718624605]
  We propose textbfReaRec, the first inference-time computing framework for recommender systems.<n>ReaRec autoregressively feeds the sequence's last hidden state into the sequential recommender.<n>We introduce two lightweight reasoning-based learning methods, Ensemble Reasoning Learning (ERL) and Progressive Reasoning Learning (PRL)
  arXiv  Detail & Related papers  (2025-03-28T17:59:03Z)
• Breaking Feedback Loops in Recommender Systems with Causal Inference [99.22185950608838]
  Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior. We propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference. We show that CAFL improves recommendation quality when compared to prior correction methods.
  arXiv  Detail & Related papers  (2022-07-04T17:58:39Z)
• Improving Sequential Query Recommendation with Immediate User Feedback [6.925738064847176]
  We propose an algorithm for next query recommendation in interactive data exploration settings. We conduct a large-scale experimental study using log files from a popular online literature discovery service.
  arXiv  Detail & Related papers  (2022-05-12T18:19:24Z)
• Replay For Safety [51.11953997546418]
  In experience replay, past transitions are stored in a memory buffer and re-used during learning. We show that using an appropriate biased sampling scheme can allow us to achieve a emphsafe policy.
  arXiv  Detail & Related papers  (2021-12-08T11:10:57Z)
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network [101.25533520688654]
  We present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time.
  arXiv  Detail & Related papers  (2020-05-21T12:28:59Z)

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.