重新定义推荐系统任务，让评估更贴近真实场景。
A Task-Centric Perspective on Recommendation Systems
- 从输入输出、时间动态等角度重构推荐任务定义。
- 指出用户决策成本与隐式交互影响模型设计。
- 适合关注评估公平性与落地实用性的研究者。
推荐系统研究常采用通用问题定义，即基于历史交互为用户推荐偏好物品，但此类抽象忽略了实际部署所需的领域特异性。而主流评测数据集多来自在线推荐平台，天然带有任务特定性。本文分析推荐任务的形成机制，强调输入输出结构、时间动态及候选物品选择等关键因素对离线评估的影响。同时探讨用户-物品交互的复杂性，包括决策成本、多步参与行为及不可观测交互，这些均可能影响模型设计。此外，论文讨论任务特异性与模型泛化能力之间的平衡，强调明确的任务定义是实现可靠评估和有效解决方案的基础。通过厘清任务定义及其影响，本工作为推荐系统研究提供结构化视角，助力研究者更好理解任务特性，确保评估的公平性与意义。
原文摘要 · Abstract (English)
Many studies in recommender systems (RecSys) adopt a general problem definition, i.e., to recommend preferred items to users based on past interactions. Such abstraction often lacks the domain-specific nuances necessary for practical deployment. However, models are frequently evaluated using datasets collected from online recommender platforms, which inherently reflect domain or task specificities. In this paper, we analyze RecSys task formulations, emphasizing key components such as input-output structures, temporal dynamics, and candidate item selection. All these factors directly impact offline evaluation. We further examine the complexities of user-item interactions, including decision-making costs, multi-step engagements, and unobservable interactions, which may influence model design. Additionally, we explore the balance between task specificity and model generalizability, highlighting how well-defined task formulations serve as the foundation for robust evaluation and effective solution development. By clarifying task definitions and their implications, this work provides a structured perspective on RecSys research. The goal is to help researchers better navigate the field, particularly in understanding specificities of the RecSys tasks and ensuring fair and meaningful evaluations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。