Recent advances in large language models have highlighted their potential for personalized recommendation, where accurately capturing user preferences remains a key challenge. Leveraging their strong reasoning and generalization capabilities, LLMs offer new opportunities for modeling long-term user behavior. To systematically evaluate this, we introduce ALPBench, a Benchmark for Attribution-level Long-term Personal Behavior Understanding. Unlike item-focused benchmarks, ALPBench predicts user-interested attribute combinations, enabling ground-truth evaluation even for newly introduced items. It models preferences from long-term historical behaviors rather than users' explicitly expressed requests, better reflecting enduring interests. User histories are represented as natural language sequences, allowing interpretable, reasoning-based personalization. ALPBench enables fine-grained evaluation of personalization by focusing on the prediction of attribute combinations task that remains highly challenging for current LLMs due to the need to capture complex interactions among multiple attributes and reason over long-term user behavior sequences.

翻译：近期大语言模型的进展凸显了其在个性化推荐领域的潜力，其中准确捕捉用户偏好仍是一个关键挑战。凭借其强大的推理与泛化能力，大语言模型为建模长期用户行为提供了新的机遇。为系统评估这一能力，我们提出了ALPBench：一个面向属性级长期个人行为理解的基准测试。与聚焦于物品的基准不同，ALPBench预测用户感兴趣的属性组合，即使对于新引入的物品也能进行真实评估。该基准通过长期历史行为而非用户明确表达的请求来建模偏好，从而更好地反映持久兴趣。用户历史被表示为自然语言序列，支持可解释的、基于推理的个性化。ALPBench通过聚焦于属性组合预测任务实现细粒度个性化评估，该任务因需捕捉多属性间的复杂交互并对长期用户行为序列进行推理，对当前大语言模型仍极具挑战性。