[2508.00450] When Relevance Meets Novelty: Dual-Stable Periodic Optimization for Serendipitous Recommendation

Computer Science > Information Retrieval arXiv:2508.00450 (cs) [Submitted on 1 Aug 2025 (v1), last revised 4 Mar 2026 (this version, v3)] Title:When Relevance Meets Novelty: Dual-Stable Periodic Optimization for Serendipitous Recommendation Authors:Hongxiang Lin, Hao Guo, Zeshun Li, Erpeng Xue, Yongqian He, Zhaoyu Hu, Lei Wang, Sheng Chen, Long Zeng View a PDF of the paper titled When Relevance Meets Novelty: Dual-Stable Periodic Optimization for Serendipitous Recommendation, by Hongxiang Lin and 8 other authors View PDF HTML (experimental) Abstract: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 pro...