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English(EN) RecSoup: Efficient Model Merging Approach for Controllable Multi-Objective Recommendation

RecSoup 引入高效模型融合以实现可控推荐系统

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 RecSoup，一种利用专业模型融合的新型两阶段方法，用于可控多目标推荐 (CMOR)。该方法通过缓解参数干扰和实现自适应参数融合，解决了将模型融合应用于推荐系统的挑战。RecSoup 旨在无需重新训练即可在推理时提供动态权衡控制，与现有的参数生成方法相比，在效率和有效性方面都有显著提高。 AI

影响 这种方法可能带来更高效、更具适应性的推荐系统，通过动态调整以适应用户偏好来改善用户体验。

排序理由 该集群包含一篇详细介绍新技术方法的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

RecSoup 引入高效模型融合以实现可控推荐系统

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zidong Jia, Guofu Xie, Xiao Zhang ·

    RecSoup：一种可控多目标推荐的高效模型融合方法

    arXiv:2610.21220v1 Announce Type: cross Abstract: Recommender systems often need to balance multiple conflicting objectives. Controllable Multi-Objective Recommendation (CMOR) enables dynamic adjustment of objective trade-offs at inference time without retraining, adapting to shi…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xiao Zhang ·

    RecSoup：可控多目标推荐的高效模型融合方法

    Recommender systems often need to balance multiple conflicting objectives. Controllable Multi-Objective Recommendation (CMOR) enables dynamic adjustment of objective trade-offs at inference time without retraining, adapting to shifting user or system requirements. Existing CMOR a…

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