Recommender systems (RecSys) leverage user interaction history to predict and suggest relevant items, shaping user experiences across various domains. While many studies 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 from online recommender platforms, which inherently reflect these 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 and loss functions. 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）利用用户交互历史来预测和推荐相关项目，从而塑造了跨多个领域的用户体验。尽管许多研究采用通用的问题定义，即基于过去的交互向用户推荐偏好项目，但这种抽象往往缺乏实际部署所需的领域特定细微差别。然而，模型通常使用来自在线推荐平台的数据集进行评估，这些数据集本质上反映了这些特性。在本文中，我们分析了推荐系统的任务表述，重点探讨了输入输出结构、时间动态和候选项目选择等关键组成部分。所有这些因素直接影响离线评估。我们进一步研究了用户-项目交互的复杂性，包括决策成本、多步参与和不可观察的交互，这些都可能影响模型设计和损失函数。此外，我们探讨了任务特定性与模型泛化能力之间的平衡，强调了明确定义的任务表述如何作为稳健评估和有效解决方案开发的基础。通过澄清任务定义及其影响，本文为推荐系统研究提供了一个结构化的视角。其目标是帮助研究人员更好地把握该领域，特别是在理解推荐系统任务的特异性以及确保公平且有意义的评估方面。