Echo Chambers in Collaborative Filtering Based Recommendation Systems

Dynamic Causal Collaborative Filtering

Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of recommending items, incorporating user feedback in model updates, and repeating the procedure. As a result, it is important to incorporate loops into the causal graphs to accurately model the dynamic and iterative data generation process for recommender systems. However, feedback loops are not always beneficial since over time they may encourage more and more narrowed content expo…

A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation

Echo chambers on social media are a significant problem that can elicit a number of negative consequences, most recently affecting the response to COVID-19. Echo chambers promote conspiracy theories about the virus and are found to be linked to vaccine hesitancy, less compliance with mask mandates, and the practice of social distancing. Moreover, the problem of echo chambers is connected to other pertinent issues like political polarization and the spread of misinformation. An echo chamber is defined as a network of users in which users only interact with opinions that support their pre-existi…

首先，不伤害：打破媒体推荐中的自杀性回音室

First, do no harm: Breaking suicidogenic echo chambers in media recommendation

针对推荐系统在心理健康场景中可能加剧用户自杀倾向的问题，提出RankAid重排序方法，通过惩罚有害内容并提升治疗性内容，在保持推荐准确性的同时确保临床安全。

Degenerate Feedback Loops in Recommender Systems

Machine learning is used extensively in recommender systems deployed in products. The decisions made by these systems can influence user beliefs and preferences which in turn affect the feedback the learning system receives - thus creating a feedback loop. This phenomenon can give rise to the so-called "echo chambers" or "filter bubbles" that have user and societal implications. In this paper, we provide a novel theoretical analysis that examines both the role of user dynamics and the behavior of recommender systems, disentangling the echo chamber from the filter bubble effect. In addition, we…

Understanding Echo Chambers in E-commerce Recommender Systems

Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper items based on user interests. However, significant efforts are missing to understand how the recommendations influence user preferences and behaviors, e.g., if and how recommendations result in \textit{echo chambers}. Extensive efforts have been made in examining the phenomenon in online media and social network systems. Meanwhile, there are growing concerns that recommender systems might lead to the self-reinforcing of…

当算法镜像心智：回音室与同质化陷阱的确认感知社会动态模型

When Algorithms Mirror Minds: A Confirmation-Aware Social Dynamic Model of Echo Chamber and Homogenization Traps

提出确认感知社会动态模型，通过整合用户心理与社交关系模拟推荐交互，理论证明回音室与同质化必然发生，并通过实证揭示系统、用户、平台三层面驱动因素及缓解策略。