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）已在众多应用中变得不可或缺，深刻影响着我们的日常体验。尽管其具有重要的现实意义，但推荐系统的学术研究常常将研究任务的定义从实际应用场景中抽象出来，旨在获得清晰的问题定义和更具普适性的研究结果。然而，我们观察到，推荐系统学术研究领域缺乏共识性理解。这一问题的根源可能在于研究任务定义的过度简化，以及过度强调对决策结果的建模，而非对决策过程本身的建模。也就是说，我们通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，而不是在动态变化且与应用场景紧密相关的上下文中预测用户下一次交互的决策。模型可访问的输入信息与用户在其决策过程中可获得的信息之间存在不匹配，但模型却被要求预测用户的决策。尽管协同过滤在从历史记录中学习用户通用偏好方面非常有效，但同样重要的是考虑实际应用中的动态上下文因素。基于特定应用场景并使用领域专用数据集来定义研究任务，可能会催生更具洞察力的发现。相应地，针对不同的应用场景，可行的解决方案和有效的评估方法也将应运而生。