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

RecSoup：可控推荐系统的高效模型融合

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的两阶段模型融合方法RecSoup，旨在增强可控多目标推荐（CMOR）系统。该方法解决了现有CMOR技术成本高或权衡能力受限的缺点。RecSoup采用专门的剪枝策略来减少不同目标参数之间的干扰，并采用自适应融合过程，允许在推理时进行动态控制。实验表明，与参数生成方法相比，RecSoup显著提高了帕累托前沿的质量和可控性，同时提供了更快的推理速度。 AI

影响 这种方法可能带来更高效、更适应性的推荐系统，通过动态调整推荐来改善用户体验。

排序理由 这是一篇详细介绍推荐系统新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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：可控推荐系统的高效模型融合

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这是一篇详细介绍推荐系统新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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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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