SOLAR : Serendipity Optimized Language Model Aligned for Recommendation (2025)

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).

## introduction recommender systems are essential tools for helping users discover items aligned with their interests. while existing recommendation algorithms @xcite @xcite focus on maximizing accuracy, they often reinforce users past preferences by recommending similar items. this accuracy-centric design leads to the well-known filter bubble phenomenon @xcite , where users are repeatedly exposed to homogeneous content, limiting exposure to novel or diverse items. over time, this can result in content fatigue and reduced user engagement. to address the filter bubble effect, the recommendation community has increasingly emphasized beyondaccuracy objectives such as diversity and serendipity @xcite . serendipity, in particular, aims to surface recommendations that are not only relevant but also novel and pleasantly surprising @xcite . various multi-objective optimization frameworks have been proposed to balance relevance with beyond-accuracy objectives @xcite . however, most existing methods still rely on heuristics and assumptions rather than real human feedback, due to the limited data for supervised training, which may not faithfully reflect human perceptions. recently, large language models (llms) have emerged as promising alternatives due to their powerful semantic understanding and broad world knowledge @xcite . unlike traditional collaborative filtering approaches that rely solely on user-item interactions, llms can directly reason over item semantics and user preferences in natural language @xcite , enabling more flexible and nuanced recommendations. this raises the exciting possibility of using llms to deliver more serendipitous recommendations by capturing subtle user intents and surfacing less obvious but relevant content. despite this potential, llms face two key challenges in serendipity-oriented recommendation. first, a domain gap exists between general-purpose llms and recommendation tasks: llms excel at natural language understanding but lack collaborative filtering mechanisms, limiting their ability to model personalized preferences from interaction data @xcite . second, there is a significant label scarcity problem, as collecting highquality serendipity annotations is costly and timeconsuming @xcite . these issues hinder the direct application of llms to person-alized, serendipity-aware recommendation scenarios. to address these challenges, we propose solar (serendipity-optimized language model aligned for recommendation), a unified framework for building llm-based recommenders optimized for serendipity. solar contains two main components: (1) a human-aligned weak supervision pipeline, where a sequential id-based recommender finetuned on limited human-labeled data generates candidate items, which are then reranked by an llm acting as a preference judge to produce large-scale, serendipity-aware pseudolabels; and (2) a domain-adaptive instruction tuning framework, termed the recommendation-specialized unified tuning network (sun), which aligns generalpurpose llms with recommendation objectives using pseudo-labeled data and carefully designed prompts. the contributions of our work are summarized as follows: ## methodology in this section, we introduce solar, a framework for building serendipity-oriented recommender system based on llms. solar addresses two key challenges in applying llms to serendipitous recommendation: the domain gap between language modeling and collaborative filtering, and the scarcity of human-labeled serendipity data. as illustrated in figure 1 , solar consists of three main stages: ## experiments in this section, we first provide our experimental setup, and then present the results as well as analyses of our proposed approach. additional implementation details are presented in appendix b. to evaluate the real world effectiveness of our approach, we also conduct an offline a/b test, whose details are provided in appendix f. we conduct experiments to answer the following research questions: ## conclusion we propose solar, a serendipity-optimized recommendation framework built upon large language models, which addresses two key challenges in serendipitous recommendation: the domain gap between language modeling and user behavior modeling, and the scarcity of serendipity-labeled data. to mitigate label scarcity, solar leverages a weak supervision strategy that combines a serendipity-tuned id-based model with an llm-based reranker to generate high-quality pseudo-labels. to bridge the domain gap, we introduce the sun framework, which aligns llms with collaborative filtering signals through domain-adaptive instruction tuning. experiments on three real-world datasets demonstrate that solar consistently outperforms strong baselines in both accuracy and serendipity. these results show the effectiveness of aligning llms with serendipitous recommendation objectives through weak supervision and instruction tuning, offering a scalable path toward more diverse and useraligned recommendation systems.