CRAMER: Control via Request-Aware Masking for Editing Recommenders

• URL: http://arxiv.org/abs/2608.25370v1
• Date: Wed, 26 Aug 2026 04:43:23 GMT
• Title: CRAMER: Control via Request-Aware Masking for Editing Recommenders
• Abstract summary: This paper presents Control via Request-Aware Masking for Editing Recommenders (CRAMER)<n>CRAMER treats user requests as control signals to modulate frozen backbone parameters through masking.<n>It achieves instant adaptation to diverse requests while avoiding costly retraining.
• Score: 3.5057035107656733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt their recommendations to the user's timely interests. Unfortunately, existing user request adaptation methods often incur high computational overhead due to either 1) retraining the entire backbone network or 2) leveraging the inference ability of large language models (a.k.a. prompt engineering), limiting their applicability in large-scale recommendation services. This paper presents Control via Request-Aware Masking for Editing Recommenders (CRAMER), a framework that takes users' natural-language requests to immediately change sequential recommendation models' behavior. Specifically, inspired by the model control theory, CRAMER treats user requests as control signals to modulate frozen backbone parameters through masking, achieving instant adaptation to diverse requests while avoiding costly retraining. Experiments on multiple large-scale benchmark datasets show that CRAMER outperforms four state-of-the-art request-aware baselines across multiple recommendation metrics while achieving minimal overhead. Moreover, the proposed framework exhibits enhanced controllability and cross-domain adaptability, establishing a new paradigm for request-aware sequential recommendation.

Related papers

• Recommender System as Slow and Fast Thinkers [12.034704337954613]
  textscDS-Frame is an adaptive fast--slow inference framework for sequential recommendation.<n>textscDS-Frame consistently improves representative sequential recommendation backbones.<n>This highlights the potential of adaptive inference for more efficient and robust recommendation.
  arXiv  Detail & Related papers  (2026-09-02T14:45:39Z)
• Multimodal Large Language Models with Adaptive Preference Optimization for Sequential Recommendation [60.33386541343322]
  We propose a Multimodal Large Language Models framework that integrates Hardness-aware and Noise-regularized preference optimization for Recommendation (HaNoRec)<n>Specifically, HaNoRec dynamically adjusts optimization weights based on both the estimated hardness of each training sample and the policy model's real-time responsiveness.
  arXiv  Detail & Related papers  (2025-11-24T04:10:46Z)
• Token-Controlled Re-ranking for Sequential Recommendation via LLMs [32.61510378078676]
  COREC is a novel token-augmented re-ranking framework that incorporates specific user requirements in co-creating the recommendation outcome.<n> COREC empowers users to steer re-ranking results with precise and flexible control via explicit, attribute-based signals.<n> Experiments show that COREC exceeds state-of-the-art baselines on standard recommendation effectiveness.
  arXiv  Detail & Related papers  (2025-11-22T04:31:19Z)
• CTRL-Rec: Controlling Recommender Systems With Natural Language [8.711335780004674]
  We propose a method that allows for natural language control of traditional recommender systems in real-time.<n>We integrate user-request-based predictions into the standard weighting of signals that traditional recommender systems optimize.<n>In a study with 19 Letterboxd users, we find that the method significantly enhanced users' sense of control and satisfaction with recommendations.
  arXiv  Detail & Related papers  (2025-10-14T17:20:04Z)
• R$^2$ec: Towards Large Recommender Models with Reasoning [59.32598867813266]
  We propose R$2$ec, a unified large recommender model with intrinsic reasoning capability.<n>R$2$ec introduces a dual-head architecture that supports both reasoning chain generation and efficient item prediction in a single model.<n>To overcome the lack of annotated reasoning data, we design RecPO, a reinforcement learning framework.
  arXiv  Detail & Related papers  (2025-05-22T17:55:43Z)
• Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control [52.405085773954596]
  Retrieval-Augmented Generation has emerged as a powerful approach to mitigate large language model hallucinations.<n>Existing RAG frameworks often apply retrieval indiscriminately,leading to inefficiencies-over-retrieving.<n>We introduce a novel user-controllable RAG framework that enables dynamic adjustment of the accuracy-cost trade-off.
  arXiv  Detail & Related papers  (2025-02-17T18:56:20Z)
• MMGRec: Multimodal Generative Recommendation with Transformer Model [81.61896141495144]
  MMGRec aims to introduce a generative paradigm into multimodal recommendation. We first devise a hierarchical quantization method Graph CF-RQVAE to assign Rec-ID for each item from its multimodal information. We then train a Transformer-based recommender to generate the Rec-IDs of user-preferred items based on historical interaction sequences.
  arXiv  Detail & Related papers  (2024-04-25T12:11:27Z)
• Intelligent Request Strategy Design in Recommender System [76.90734681369156]
  We envision a new learning task of edge intelligence named Intelligent Request Strategy Design (IRSD) IRSD aims to improve the effectiveness of waterfall RSs by determining the appropriate occasions of request insertion based on users' real-time intention. We propose a new paradigm of adaptive request insertion strategy named Uplift-based On-edge Smart Request Framework (AdaRequest)
  arXiv  Detail & Related papers  (2022-06-23T16:51:38Z)
• 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)
• Self-Supervised Reinforcement Learning for Recommender Systems [77.38665506495553]
  We propose self-supervised reinforcement learning for sequential recommendation tasks. Our approach augments standard recommendation models with two output layers: one for self-supervised learning and the other for RL. Based on such an approach, we propose two frameworks namely Self-Supervised Q-learning(SQN) and Self-Supervised Actor-Critic(SAC)
  arXiv  Detail & Related papers  (2020-06-10T11:18:57Z)
• Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
  We formalize the recommender system as a sequential recommendation problem. We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
  arXiv  Detail & Related papers  (2020-05-19T10:18:43Z)

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.