Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Areeb, Qazi Mohammad; Nadeem, Mohammad; Sohail, Shahab Saquib; Imam, Raza; Doctor, Faiyaz; Himeur, Yassine; Hussain, Amir; Amira, Abbes

doi:10.1002/widm.1512

Cited by 15 publications

(1 citation statement)

References 104 publications

(234 reference statements)

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“…The high cost of healthcare data also poses difficulties for doctors to track patients and manage a large patient volume with various treatment options. Given these challenges, the implementation of recommender systems (RSs) has been proposed to improve decision-making in healthcare and ease the workload for both patients and oncologists [335,336]. The use of RS in digital health provides personalized recommendations, an accurate analysis of big data, and stronger privacy protection through integration with AI and machine learning technologies [337] as depicted in Figure 14.…”

Section: Recommender Systems (Rss)mentioning

confidence: 99%

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Habchi,

Himeur,

Kheddar

et al. 2023

Systems

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Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years, offering advanced tools and methodologies that promise to revolutionize patient outcomes. This review provides an exhaustive overview of the contemporary frameworks employed in the field, focusing on the objective of AI-driven analysis and dissecting methodologies across supervised, unsupervised, and ensemble learning. Specifically, we delve into techniques such as deep learning, artificial neural networks, traditional classification, and probabilistic models (PMs) under supervised learning. With its prowess in clustering and dimensionality reduction, unsupervised learning (USL) is explored alongside ensemble methods, including bagging and potent boosting algorithms. The thyroid cancer datasets (TCDs) are integral to our discussion, shedding light on vital features and elucidating feature selection and extraction techniques critical for AI-driven diagnostic systems. We lay out the standard assessment criteria across classification, regression, statistical, computer vision, and ranking metrics, punctuating the discourse with a real-world example of thyroid cancer detection using AI. Additionally, this study culminates in a critical analysis, elucidating current limitations and delineating the path forward by highlighting open challenges and prospective research avenues. Through this comprehensive exploration, we aim to offer readers a panoramic view of AI’s transformative role in thyroid cancer diagnosis, underscoring its potential and pointing toward an optimistic future.

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Section: Recommender Systems (Rss)mentioning

confidence: 99%

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Habchi,

Himeur,

Kheddar

et al. 2023

Systems

Self Cite

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Prick “filter bubbles” by enhancing consumers' novelty‐seeking: The role of personalized recommendations of unmentionable products

Lv,

Kang,

Liu

2024

Psychology and Marketing

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Personalized recommendation algorithms inadvertently foster “filter bubbles,” wherein consumers are predominantly exposed to information that aligns with their existing preferences, limiting their exposure to novel items. This phenomenon raises ethical concerns regarding consumer well‐being, as it potentially compromises the quality of consumption decisions by reinforcing a homogeneity of information. Introducing novelty into recommendation systems is a viable strategy to counteract this issue, as the predominance of homogeneous information plays a crucial role in the formation of filter bubbles. However, there is a notable gap in the literature regarding self‐directed strategies for consumers to break through these filter bubbles. Grounded in social identification theory and utilizing a series of experimental studies, our research employs a range of analytical techniques, including ANOVA, mediation, and moderated‐mediation analysis. Our findings suggest that personalized recommendations of unmentionable products, defined as products eliciting disgust, offense, or anger due to delicacy, ethics, or fear, (vs. ordinary products) can increase consumers' novelty‐seeking by enhancing their motivation to change their implicit social labels given by intelligent recommendation systems. Nonetheless, we observe that this drive for novelty‐seeking diminishes during social‐focused recommendations because this recommendation is based on the behaviors of others in consumers' social networks rather than their actions.

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