ALPBench：一种用于属性级长期个人行为理解的基准测试
ALPBench: A Benchmark for Attribution-level Long-term Personal Behavior Understanding
AI总结：
ALPBench是一种用于属性级长期个人行为理解的基准测试，通过预测用户感兴趣的属性组合，实现对个性化推荐的细粒度评估。
摘要：
近年来，大语言模型的进步凸显了其在个性化推荐中的潜力，其中准确捕捉用户偏好仍然是一个关键挑战。利用其强大的推理和泛化能力，LLMs为建模长期用户行为提供了新机会。为了系统评估这一方面，我们引入了ALPBench，一种用于属性级长期个人行为理解的基准测试。不同于以物品为中心的基准测试，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实现真实评价。它从长期历史行为中建模偏好，而不是用户明确表达的要求，更能反映持久兴趣。用户历史被表示为自然语言序列，允许可解释、基于推理的个性化。ALPBench通过聚焦于属性组合预测任务来实现细粒度的个性化评估，该任务对当前LLMs来说仍然极具挑战性，因为需要捕捉多个属性之间的复杂交互并推理长期用户行为序列。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
