Recommender Systems (RecSys) have become indispensable in numerous applications, profoundly influencing our everyday experiences. Despite their practical significance, academic research in RecSys often abstracts the formulation of research tasks from real-world contexts, aiming for a clean problem formulation and more generalizable findings. However, it is observed that there is a lack of collective understanding in RecSys academic research. The root of this issue may lie in the simplification of research task definitions, and an overemphasis on modeling the decision outcomes rather than the decision-making process. That is, we often conceptualize RecSys as the task of predicting missing values in a static user-item interaction matrix, rather than predicting a user's decision on the next interaction within a dynamic, changing, and application-specific context. There exists a mismatch between the inputs accessible to a model and the information available to users during their decision-making process, yet the model is tasked to predict users' decisions. While collaborative filtering is effective in learning general preferences from historical records, it is crucial to also consider the dynamic contextual factors in practical settings. Defining research tasks based on application scenarios using domain-specific datasets may lead to more insightful findings. Accordingly, viable solutions and effective evaluations can emerge for different application scenarios.

翻译：推荐系统（RecSys）已在众多应用中不可或缺，深刻影响着我们的日常体验。尽管具有重要的实践意义，但推荐系统的学术研究常将研究任务的定义从现实场景中抽象出来，以追求清晰的问题定义和更可推广的结论。然而，我们注意到推荐系统学术研究中缺乏共同理解。这一问题的根源可能在于研究任务定义的简化，以及过度强调对决策结果建模而非决策过程。即，我们常将推荐系统概念化为预测静态用户-物品交互矩阵中缺失值的任务，而非在动态、变化且依赖具体应用场景的语境中预测用户下一次交互的决策。模型可访问的输入与用户决策过程中可获得的信息之间存在错配，而模型却被要求预测用户决策。尽管协同过滤能有效从历史记录中学习通用偏好，但实践场景中的动态情境因素同样至关重要。基于特定领域数据集的应用场景定义研究任务，或能带来更具洞察力的发现。由此，不同应用场景中将涌现出可行的解决方案与有效的评估方法。