当相关性与新颖性交汇：为偶然推荐的双稳定周期优化
When Relevance Meets Novelty: Dual-Stable Periodic Optimization for Serendipitous Recommendation
AI总结：
本文提出CoEA方法，通过双稳定周期优化解决推荐系统中相关性与新颖性的平衡问题，提升偶然推荐效果。
摘要：
传统推荐系统倾向于通过过度推送与用户历史偏好一致的内容，将用户困在强烈的反馈循环中，从而限制了探索机会并导致内容疲劳。尽管大型语言模型（LLMs）凭借其多样化的内容生成能力展现出潜力，但现有的LLM增强的双模型框架面临两个主要局限：首先，它们忽略了由群体身份驱动的长期偏好，导致兴趣建模偏颇；其次，它们存在静态优化的缺陷，因为一次性的对齐过程无法利用增量用户数据进行闭环优化。为了解决这些挑战，我们提出了Co-Evolutionary Alignment（CoEA）方法。针对兴趣建模偏颇问题，我们引入了Dual-Stable Interest Exploration（DSIE）模块，通过并行处理行为序列，联合建模长期群体身份和短期个体兴趣。针对静态优化限制问题，我们设计了Periodic Collaborative Optimization（PCO）机制。该机制定期使用Relevance LLM对增量数据进行偏好验证，然后引导Novelty LLM根据验证结果进行微调，并随后将持续微调的Novelty LLM的输出反馈给Relevance LLM进行重新评估，从而实现动态闭环优化。广泛的在线和离线实验验证了CoEA模型在偶然推荐中的有效性。
英文摘要：
Traditional recommendation systems tend to trap users in strong feedback loops by excessively pushing content aligned with their historical preferences, thereby limiting exploration opportunities and causing content fatigue. Although large language models (LLMs) demonstrate potential with their diverse content generation capabilities, existing LLM-enhanced dual-model frameworks face two major limitations: first, they overlook long-term preferences driven by group identity, leading to biased interest modeling; second, they suffer from static optimization flaws, as a one-time alignment process fails to leverage incremental user data for closed-loop optimization. To address these challenges, we propose the Co-Evolutionary Alignment (CoEA) method. For interest modeling bias, we introduce Dual-Stable Interest Exploration (DSIE) module, jointly modeling long-term group identity and short-term individual interests through parallel processing of behavioral sequences. For static optimization limitations, we design a Periodic Collaborative Optimization (PCO) mechanism. This mechanism regularly conducts preference verification on incremental data using the Relevance LLM, then guides the Novelty LLM to perform fine-tuning based on the verification results, and subsequently feeds back the output of the continually fine-tuned Novelty LLM to the Relevance LLM for re-evaluation, thereby achieving a dynamic closed-loop optimization. Extensive online and offline experiments verify the effectiveness of the CoEA model in serendipitous recommendation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
