Quantifying and mitigating the spiral of silence in recommender systems: A modular probabilistic framework

Online rating systems are an indispensable building block for many web applications, yet they are pervasively affected by data biases that distort our understanding of user preferences. A key source of this bias is the “Spiral of Silence” (SoS) phenomenon, where users who perceive their opinions to be in the minority tend to withhold them, leading to non-random missing data. While initial research has begun to explore this issue, existing SoS models for recommender systems fail to capture real-world complexities, such as rating recency, user heterogeneity (e.g., “hardcore” users immune to social pressure), and asymmetric responses to conforming versus dissenting opinions. Furthermore, these models often lack formal theoretical guarantees. To fill this gap, we introduce Bi-MCP, a modular probabilistic framework to quantify and mitigate SoS effects. Bi-MCP models a bifurcated user population (hardcore vs. conformist) and their bidirectional (asymmetric) response to the perceived opinion climate. The framework is (i) Expressive, capturing novel factors missed by prior work; (ii) Modular, allowing its components to be extended or replaced to support future research; and (iii) Theoretically grounded, with a formal proof of convergence for its Generalized Expectation-Maximization (GEM) inference algorithm. Experimental results on four large-scale public datasets demonstrate that our model significantly improves recommendation accuracy over state-of-the-art baselines, showcasing the practical benefit of explicitly modeling these nuanced SoS effects.