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.

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