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.

翻译：推荐系统（RecSys）领域的许多研究采用通用的问题定义，即基于历史交互向用户推荐偏好物品。这种抽象往往缺乏实际部署所需的领域特定细微差异。然而，模型评估常使用从在线推荐平台收集的数据集，这些数据本质上反映了领域或任务的特定性。本文分析了推荐系统的任务构建框架，重点探讨输入输出结构、时序动态性和候选物品选择等关键组成部分。这些因素均直接影响离线评估效果。我们进一步研究了用户-物品交互的复杂性，包括决策成本、多步参与和不可观测交互等可能影响模型设计的因素。此外，我们探讨了任务特定性与模型泛化能力之间的平衡，强调明确定义的任务构建是稳健评估和有效解决方案开发的基础。通过厘清任务定义及其影响，本研究为推荐系统研究提供了结构化视角，旨在帮助研究者更好地把握该领域，特别是在理解推荐系统任务特定性及确保公平有效评估方面提供指引。