Beyond Collaborative Filtering: A Relook at Task Formulation in Recommender Systems

Abstract

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.