Adaptive Implicit Feedback Weighting: Addressing Signal Saturation and User Heterogeneity in Collaborative Filtering

Recommender systems increasingly rely on implicit feedback (e.g., clicks, play counts, watch times) due to the scarcity of explicit ratings. However, standard collaborative filtering models, such as Weighted Matrix Factorization, traditionally assume that a user’s preference grows continuously and unboundedly with raw interaction counts. This “one-size-fits-all” assumption ignores the severe distributional skew caused by signal saturation and user heterogeneity, where identical interaction volumes can represent vastly different preference intensities. In this paper, we challenge static scaling assumptions by proposing a suite of dynamic, adaptive confidence weighting strategies designed to stabilize latent representations. We introduce the Power-Lift strategy, a numerically stable generalization of Pointwise Mutual Information that captures the probabilistic surprise of interactions, alongside Robust User-Centric weighting, which normalizes frequencies utilizing local, non-parametric user statistics. Furthermore, we formalize the Saturation Hypothesis, demonstrating that sigmoid-based limits on confidence successfully prevent extreme behavioral outliers from distorting latent space geometries. Through rigorous offline evaluation across five diverse datasets, we show that our adaptive weighting approaches consistently outperform traditional unweighted baselines and standard Information Retrieval scaling methods. Notably, our Power-Lift strategy achieves up to a 54.3% relative improvement in Normalized Discounted Cumulative Gain at rank 20 on the highly skewed Steam dataset. Ultimately, these methods provide a highly scalable, computationally efficient mechanism to boost top-N recommendation accuracy and reduce required model capacity without altering downstream architectural inference latency.