Ben (Liben) Chen

Data privacy regulations empower consumers to control the collection of their personal data. A significant consequence of these regulations is their effect on various data-driven business solutions, especially on personalization technologies and recommender systems. We investigate the potential impacts of diverse real-world data privacy practices (that can be adopted by firms in response to various data privacy regulations) on the recommender systems performance. We also examine how these impacts vary across different personalization contexts and applications. In particular, we distinguish between scenarios where the user population exhibits more stable vs. more dynamic (i.e., changing) preferences, as these scenarios often represent distinctly different recommendation settings. We use a simulation framework, carefully seeded with real-world data, for a comprehensive evaluation of the recommender system performance under numerous scenarios, such as different recommendation algorithms, different data privacy practices, different degrees of users’ preference dynamics, etc. Extensive computational experiments uncover several robust performance patterns for different data privacy practices and highlight substantial differences between recommendation settings with stable vs. changing user preferences. Furthermore, building upon the comprehensive findings from our computational experiments, we propose flexible, parameterizable data privacy practice designs based on the notion of revelation protection. The proposed approach is designed to provide an effective balance between personalization performance and privacy considerations, as demonstrated using extensive simulation experiments. The findings of this study have significant implications for the design of privacy-aware recommender systems in the context of contemporary data privacy regulations. The findings can also be informative to policymakers for understanding the practical implications of various data privacy practices and for designing future policies.

Preference bias is a well-established phenomenon in recommender systems (RS), where the system-predicted rating that the user observes as part of the item recommendation (i.e., before consuming the item) ends up systematically impacting the self-reported preference rating that a user provides as feedback after consuming the item. This paper investigates how user preference bias amplifies and prolongs the adverse effects of external perturbations (e.g., shilling attacks) in RS. While prior research has treated preference bias and external manipulations as separate phenomena, we demonstrate through a series of agent-based simulation experiments that their interaction produces self-reinforcing adverse effects. Specifically, we simulate perturbation scenarios where a small subset of items is temporarily promoted through artificial rating inflation. We systematically vary user preference bias levels, the proportion of perturbed users and items, and the duration of perturbation across multiple experimental conditions. Our experiments reveal that even small-scale and transient perturbations can have lasting consequences on RS performance, especially when user preference bias is strong. Perturbed items continue to dominate recommendation lists long after the external shock ends, as biased user feedback reinforces and sustains their artificial prominence. This creates prolonged degradation of predictive accuracy as well as reduced relevance and diversity of consumed items. Importantly, the adverse effects are consistently present across different recommendation algorithms and rating datasets. We also find that unpopular and content-wise common items are especially susceptible to these adverse effects. We further demonstrate these adverse effects under different perturbation strategies. These findings have significant implications for RS design, as they underscore the potential limitations of traditional perturbation defenses (i.e., that may overlook the role of user preference bias) and highlight the need for bias-aware robustness and mitigation frameworks.

Suppose a machine learning (ML) model is re-trained after the arrival of some new data; however, all the new data instances are such that the model’s prior predictions for them were perfectly accurate. Are the predictions of the updated model consistent with its prior predictions? Such self-consistency of an ML system is an important factor for users’ acceptance of and trust in the ML systems. In this study, we design an ensemble approach for improving the self-consistency of the ML system, based on a smoothing technique. Smoothing adds a pseudo-labeled dataset with the model’s own predictions into the original training data with an explicit goal of improving self-consistency. Our ensemble approach uses smoothing in a prediction-confidence-aware manner and significantly outperforms generic smoothing methods in terms of both self-consistency and predictive performance. This study contributes to understanding the nuances of self-consistency enhancement in ML systems and provides practical insights for designing more robust and trustworthy decision support systems.