@inproceedings{yuan-etal-2025-solar,
    title = "{SOLAR}: Serendipity Optimized Language Model Aligned for Recommendation",
    author = "Yuan, Zichen  and
      Sun, Lifan  and
      Zhuang, Yucen  and
      Wang, Yue  and
      Song, Xinyuan  and
      Xu, Tianqi  and
      Li, Siyuan  and
      Fu, Junchen  and
      Li, Youhua  and
      Hong, Sirui  and
      Chen, Jiaqi  and
      Jose, Joemon M.  and
      Ni, Yongxin",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.538/",
    doi = "10.18653/v1/2025.findings-emnlp.538",
    pages = "10146--10169",
    ISBN = "979-8-89176-335-7",
    abstract = "Recently, Large Language Models (LLMs) have shown strong potential in recommendation tasks due to their broad world knowledge and reasoning capabilities. However, applying them to serendipity-oriented recommendation remains challenging, mainly due to a domain gap of LLMs in modeling personalized user behavior and the scarcity of labeled serendipitous interactions. In this paper, we introduce **SOLAR** (**S**erendipity-**O**ptimized **L**anguage model **A**ligned for **R**ecommendation), a two-stage framework that addresses these challenges. To alleviate label scarcity, we adopt a weak supervision strategy: a sequential ID-based recommender generates candidate items, which are then reranked by an LLM acting as a preference judge to produce serendipity-aware pseudo-labels. To bridge the domain gap, we propose a domain-adaptive instruction tuning method (SUN) that aligns LLMs with recommendation tasks. Experiments on three real-world datasets show that **SOLAR** consistently improves both accuracy and serendipity over strong baselines, showing its effectiveness in enabling more diverse, user-centric recommendations. Code and dataset are released at [https://github.com/SOLAR2025ARR/SOLAR](https://github.com/SOLAR2025ARR/SOLAR)."
}