@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://aclanthology.org/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 \textbf{SOLAR} (\textbf{S}erendipity-\textbf{O}ptimized \textbf{L}anguage model \textbf{A}ligned for \textbf{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 \textbf{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 \url{https://github.com/SOLAR2025ARR/SOLAR}."
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<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 (Serendipity-Optimized Language model Aligned for Recommendation), 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.</abstract>
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%0 Conference Proceedings
%T SOLAR: Serendipity Optimized Language Model Aligned for Recommendation
%A Yuan, Zichen
%A Sun, Lifan
%A Zhuang, Yucen
%A Wang, Yue
%A Song, Xinyuan
%A Xu, Tianqi
%A Li, Siyuan
%A Fu, Junchen
%A Li, Youhua
%A Hong, Sirui
%A Chen, Jiaqi
%A Jose, Joemon M.
%A Ni, Yongxin
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F yuan-etal-2025-solar
%X 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 (Serendipity-Optimized Language model Aligned for Recommendation), 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.
%R 10.18653/v1/2025.findings-emnlp.538
%U https://aclanthology.org/2025.findings-emnlp.538/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.538
%P 10146-10169
Markdown (Informal)
[SOLAR: Serendipity Optimized Language Model Aligned for Recommendation](https://aclanthology.org/2025.findings-emnlp.538/) (Yuan et al., Findings 2025)
ACL
- Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, and Yongxin Ni. 2025. SOLAR: Serendipity Optimized Language Model Aligned for Recommendation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 10146–10169, Suzhou, China. Association for Computational Linguistics.