Modeling and Debiasing Temporal Popularity in Recommendation with Counting Processes

Implicit-feedback recommender systems are trained on logged interactions in which observed choices conflate user-specific preference with time-varying item popularity. We formulate timestamped user-item interactions as counting processes and decompose the conditional interaction intensity into user activity, dynamic item popularity, and user-specific utility. This formulation reveals an identifiability issue: without additional structure, item-time effects shared across users can be absorbed into the utility function. We show that centering the utility across users resolves this ambiguity up to ranking-equivalent shifts. We further establish that unmodeled popularity can induce a shared direction in user-context representations, leading to geometric homogenization and degraded uniformity. Motivated by this analysis, we propose a general counting process framework that jointly addresses the challenging tasks of debiasing temporal item popularity and identifying user–item-specific effects. Experiments on synthetic and real-world datasets support the theoretical analysis and show consistent improvements across diverse recommendation backbones.