CoNRec: Context-Discerning Negative Recommendation with LLMs

• URL: http://arxiv.org/abs/2601.15721v1
• Date: Thu, 22 Jan 2026 07:46:18 GMT
• Title: CoNRec: Context-Discerning Negative Recommendation with LLMs
• Authors: Xinda Chen, Jiawei Wu, Yishuang Liu, Jialin Zhu, Shuwen Xiao, Junjun Zheng, Xiangheng Kong, Yuning Jiang,
• Abstract summary: Research into users' negative preferences has gained increasing importance in modern recommendation systems.<n>Most existing approaches primarily use negative feedback as an auxiliary signal to enhance positive recommendations.<n>We propose the first large language model framework for negative feedback modeling with special designed context-discerning modules.
• Score: 5.832474387562381
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding what users like is relatively straightforward; understanding what users dislike, however, remains a challenging and underexplored problem. Research into users' negative preferences has gained increasing importance in modern recommendation systems. Numerous platforms have introduced explicit negative feedback mechanisms and leverage such signals to refine their recommendation models. Beyond traditional business metrics, user experience-driven metrics, such as negative feedback rates, have become critical indicators for evaluating system performance. However, most existing approaches primarily use negative feedback as an auxiliary signal to enhance positive recommendations, paying little attention to directly modeling negative interests, which can be highly valuable in offline applications. Moreover, due to the inherent sparsity of negative feedback data, models often suffer from context understanding biases induced by positive feedback dominance. To address these challenges, we propose the first large language model framework for negative feedback modeling with special designed context-discerning modules. We use semantic ID Representation to replace text-based item descriptions and introduce an item-level alignment task that enhances the LLM's understanding of the semantic context behind negative feedback. Furthermore, we design a Progressive GRPO training paradigm that enables the model to dynamically balance the positive and negative behavioral context utilization. Besides, our investigation further reveals a fundamental misalignment between the conventional next-negative-item prediction objective and users' true negative preferences, which is heavily influenced by the system's recommendation order. To mitigate this, we propose a novel reward function and evaluation metric grounded in multi-day future negative feedback and their collaborative signals.

Related papers

• Towards Reliable Negative Sampling for Recommendation with Implicit Feedback via In-Community Popularity [8.257297407777555]
  We propose textbfICPNS (In-Community Popularity Negative Sampling) to identify reliable and informative negative samples.<n>Our approach is grounded in the insight that item exposure is driven by latent user communities.<n>ICPNS yields consistent improvements on graph-based recommenders and competitive performance on MF-based models.
  arXiv  Detail & Related papers  (2026-02-21T08:53:10Z)
• Improving LLM-based Recommendation with Self-Hard Negatives from Intermediate Layers [80.55429742713623]
  ILRec is a novel preference fine-tuning framework for LLM-based recommender systems.<n>We introduce a lightweight collaborative filtering model to assign token-level rewards for negative signals.<n>Experiments on three datasets demonstrate ILRec's effectiveness in enhancing the performance of LLM-based recommender systems.
  arXiv  Detail & Related papers  (2026-02-19T14:37:43Z)
• Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns [1.6044444452278062]
  We consider the task of learning from both positive and negative feedback in a sequential recommendation scenario.<n>In this work, we propose to train two transformer encoders on separate positive and negative interaction sequences.<n>We demonstrate the effectiveness of this approach in terms of increasing true-positive metrics compared to state-of-the-art sequential recommendation methods.
  arXiv  Detail & Related papers  (2025-08-20T15:32:16Z)
• Learning Recommender Systems with Soft Target: A Decoupled Perspective [49.83787742587449]
  We propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels. We present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors.
  arXiv  Detail & Related papers  (2024-10-09T04:20:15Z)
• Negative Sampling in Recommendation: A Survey and Future Directions [43.11318243903388]
  Recommender system (RS) aims to capture personalized preferences from massive user behaviors.<n>Negative sampling is proficients in revealing the genuine negative aspect inherent in user behaviors.
  arXiv  Detail & Related papers  (2024-09-11T12:48:52Z)
• Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation [67.88747330066049]
  Fine-grained feedback captures nuanced distinctions in image quality and prompt-alignment. We show that demonstrating its superiority to coarse-grained feedback is not automatic. We identify key challenges in eliciting and utilizing fine-grained feedback.
  arXiv  Detail & Related papers  (2024-06-24T17:19:34Z)
• RLVF: Learning from Verbal Feedback without Overgeneralization [94.19501420241188]
  We study the problem of incorporating verbal feedback without such overgeneralization. We develop a new method Contextualized Critiques with Constrained Preference Optimization (C3PO) Our approach effectively applies verbal feedback to relevant scenarios while preserving existing behaviors for other contexts.
  arXiv  Detail & Related papers  (2024-02-16T18:50:24Z)
• DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops [41.21024436158042]
  We study the negative impact of feedback loops and unknown exposure mechanisms on recommendation quality and user experience. We propose Dynamic Personalized Ranking (textbfDPR), an unbiased algorithm that uses dynamic re-weighting to mitigate the cross-effects. We show theoretically that our approach mitigates the negative effects of feedback loops and unknown exposure mechanisms.
  arXiv  Detail & Related papers  (2023-11-10T04:36:00Z)
• Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders [13.762960304406016]
  We introduce explicit and implicit negative user feedback into the training objective of sequential recommenders. We demonstrate the effectiveness of this approach using live experiments on a large-scale industrial recommender system.
  arXiv  Detail & Related papers  (2023-08-23T17:16:07Z)
• Generating Negative Samples for Sequential Recommendation [83.60655196391855]
  We propose to Generate Negative Samples (items) for Sequential Recommendation (SR) A negative item is sampled at each time step based on the current SR model's learned user preferences toward items. Experiments on four public datasets verify the importance of providing high-quality negative samples for SR.
  arXiv  Detail & Related papers  (2022-08-07T05:44:13Z)
• Reinforced Negative Sampling over Knowledge Graph for Recommendation [106.07209348727564]
  We develop a new negative sampling model, Knowledge Graph Policy Network (kgPolicy), which works as a reinforcement learning agent to explore high-quality negatives. kgPolicy navigates from the target positive interaction, adaptively receives knowledge-aware negative signals, and ultimately yields a potential negative item to train the recommender.
  arXiv  Detail & Related papers  (2020-03-12T12:44:30Z)

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.