Token-Controlled Re-ranking for Sequential Recommendation via LLMs

• URL: http://arxiv.org/abs/2511.17913v1
• Date: Sat, 22 Nov 2025 04:31:19 GMT
• Title: Token-Controlled Re-ranking for Sequential Recommendation via LLMs
• Authors: Wenxi Dai, Wujiang Xu, Pinhuan Wang, Dimitris N. Metaxas,
• Abstract summary: 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.
• Score: 32.61510378078676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The widespread adoption of Large Language Models (LLMs) as re-rankers is shifting recommender systems towards a user-centric paradigm. However, a significant gap remains: current re-rankers often lack mechanisms for fine-grained user control. They struggle to balance inherent user preferences with multiple attribute-based constraints, often resorting to simplistic hard filtering that can excessively narrow the recommendation pool and yield suboptimal results. This limitation leaves users as passive recipients rather than active collaborators in the recommendation process. To bridge this gap, we propose COREC, a novel token-augmented re-ranking framework that incorporates specific user requirements in co-creating the recommendation outcome. COREC empowers users to steer re-ranking results with precise and flexible control via explicit, attribute-based signals. The framework learns to balance these commands against latent preferences, yielding rankings that adhere to user instructions without sacrificing personalization. Experiments show that COREC: (1) exceeds state-of-the-art baselines on standard recommendation effectiveness and (2) demonstrates superior adherence to specific attribute requirements, proving that COREC enables fine-grained and predictable manipulation of the rankings.

Related papers

• RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders [0.8246494848934447]
  We propose a fine-tuning solution using human feedback reinforcement learning (RLHF) to maximize implied user feedback (IUF) in a multi-turn recommendation context.<n>We show that our RLHF-fine-tuned models can perform better in terms of top-$k$ recommendation accuracy, coherence, and user satisfaction compared to (arrow-zero-cmwrquca-teja-falset ensuite 2Round group-deca States penalty give up.
  arXiv  Detail & Related papers  (2025-08-07T11:36:55Z)
• Embed Progressive Implicit Preference in Unified Space for Deep Collaborative Filtering [13.24227546548424]
  Generalized Neural Ordinal Logistic Regression (GNOLR) is proposed to capture the structured progression of user engagement.<n>GNOLR enhances predictive accuracy, captures the progression of user engagement, and simplifies the retrieval process.<n>Experiments on ten real-world datasets show that GNOLR significantly outperforms state-of-the-art methods in efficiency and adaptability.
  arXiv  Detail & Related papers  (2025-05-27T08:43:35Z)
• A Novel Generative Model with Causality Constraint for Mitigating Biases in Recommender Systems [20.672668625179526]
  Latent confounding bias can obscure the true causal relationship between user feedback and item exposure.<n>We propose a novel generative framework called Latent Causality Constraints for Debiasing representation learning in Recommender Systems.
  arXiv  Detail & Related papers  (2025-05-22T14:09:39Z)
• Multi-agents based User Values Mining for Recommendation [52.26100802380767]
  We propose a zero-shot multi-LLM collaborative framework for effective and accurate user value extraction.<n>We apply text summarization techniques to condense item content while preserving essential meaning.<n>To mitigate hallucinations, we introduce two specialized agent roles: evaluators and supervisors.
  arXiv  Detail & Related papers  (2025-05-02T04:01:31Z)
• Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User [117.82681846559909]
  Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations.<n>We propose a generative reward model based simulated user, named GRSU, for automatic interaction with CRSs.
  arXiv  Detail & Related papers  (2025-04-29T06:37:30Z)
• Reason4Rec: Large Language Models for Recommendation with Deliberative User Preference Alignment [69.11529841118671]
  We propose a new Deliberative Recommendation task, which incorporates explicit reasoning about user preferences as an additional alignment goal.<n>We then introduce the Reasoning-powered Recommender framework for deliberative user preference alignment.
  arXiv  Detail & Related papers  (2025-02-04T07:17:54Z)
• Reasoning over User Preferences: Knowledge Graph-Augmented LLMs for Explainable Conversational Recommendations [58.61021630938566]
  Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by capturing user preferences through interactive dialogues.<n>Current CRSs often leverage knowledge graphs (KGs) or language models to extract and represent user preferences as latent vectors, which limits their explainability.<n>We propose a plug-and-play framework that synergizes LLMs and KGs to reason over user preferences, enhancing the performance and explainability of existing CRSs.
  arXiv  Detail & Related papers  (2024-11-16T11:47:21Z)
• Preference Diffusion for Recommendation [50.8692409346126]
  We propose PreferDiff, a tailored optimization objective for DM-based recommenders.<n> PreferDiff transforms BPR into a log-likelihood ranking objective to better capture user preferences.<n>It is the first personalized ranking loss designed specifically for DM-based recommenders.
  arXiv  Detail & Related papers  (2024-10-17T01:02:04Z)
• DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems [9.433227503973077]
  We propose a novel framework designed to enable fine-grained control over diversity in LLM-based recommendations.<n>Unlike traditional methods, DLCRec adopts a fine-grained task decomposition strategy, breaking down the recommendation process into three sub-tasks.<n>We introduce two data augmentation techniques that enhance the model's robustness to noisy and out-of-distribution data.
  arXiv  Detail & Related papers  (2024-08-22T15:10:56Z)
• Customizing Language Models with Instance-wise LoRA for Sequential Recommendation [28.667247613039965]
  Sequential recommendation systems predict the next interaction item based on users' past interactions, aligning recommendations with individual preferences.<n>We propose Instance-wise LoRA (iLoRA) as a form of multi-task learning, integrating LoRA with the Mixture of Experts (MoE) framework.<n>iLoRA achieves an average relative improvement of 11.4% over basic LoRA in the hit ratio metric, with less than a 1% relative increase in trainable parameters.
  arXiv  Detail & Related papers  (2024-08-19T17:09:32Z)
• User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations [96.45414741693119]
  We present a user-controllable recommender system that seamlessly integrates explainability and controllability. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system.
  arXiv  Detail & Related papers  (2023-08-02T01:13:36Z)

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.