Trust and Trustworthiness in Social Recommender Systems

Hassan, Taha

doi:10.1145/3308560.3317596

Cited by 34 publications

(12 citation statements)

References 14 publications

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“…The use of NLP algorithms seems effective in helping students develop algorithmic literacy (Koenig, 2020; Long & Magerko, 2020; Darvishi et al, 2022); however, inaccurate prompts and recommendations by the system (eg, asking a reviewer to add an explicit suggestion, while the review already has one) may lead to students and instructors losing their trust in the system. An interesting future direction is to explore best practices for developing trustworthy recommender systems (Hassan, 2019).…”

Section: Discussionmentioning

confidence: 99%

Incorporating AI and learning analytics to build trustworthy peer assessment systems

Darvishi

Khosravi

Sadiq

et al. 2022

Brit J Educational Tech

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Peer assessment has been recognised as a sustainable and scalable assessment method that promotes higher‐order learning and provides students with fast and detailed feedback on their work. Despite these benefits, some common concerns and criticisms are associated with the use of peer assessments (eg, scarcity of high‐quality feedback from peer student‐assessors and lack of accuracy in assigning a grade to the assessee) that raise questions about their trustworthiness. Consequently, many instructors and educational institutions have been anxious about incorporating peer assessment into their teaching. This paper aims to contribute to the growing literature on how AI and learning analytics may be incorporated to address some of the common concerns associated with peer assessment systems, which in turn can increase their trustworthiness and adoption. In particular, we present and evaluate our AI‐assisted and analytical approaches that aim to (1) offer guidelines and assistance to student‐assessors during individual reviews to provide better feedback, (2) integrate probabilistic and text analysis inference models to improve the accuracy of the assigned grades, (3) develop feedback on reviews strategies that enable peer assessors to review the work of each other, and (4) employ a spot‐checking mechanism to assist instructors in optimally overseeing the peer assessment process. What is already known about this topic Engaging students in peer assessment has been demonstrated to have various benefits. However, there are some common concerns associated with employing peer assessment that raise questions about their trustworthiness as an assessment item. What this paper adds Methods and processes on how AI and learning analytics may be incorporated to address some of the common concerns associated with peer assessment systems, which in turn, can increase their trustworthiness and adoption. Implications for practice Presentation of a systematic approach for development, deployment and evaluation of AI and analytics approaches in peer assessment systems.

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Section: Discussionmentioning

confidence: 99%

Incorporating AI and learning analytics to build trustworthy peer assessment systems

Darvishi

Khosravi

Sadiq

et al. 2022

Brit J Educational Tech

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“…Social Recommender Systems (SRS) are recommender systems [47,45] that target the social media domain. They aim at coping with the social overload challenge by presenting the most relevant and attractive data to the user, typically by applying personalization techniques.…”

Section: F Social Based Recommender Systemmentioning

confidence: 99%

H2E: A Privacy Provisioning Framework for Collaborative Filtering Recommender System

Ashraf¹,

Naeem²,

Javed³

et al. 2019

IJMECS

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A Recommender System (RS) is the most significant technologies that handle the information overload problem of Retrieval Information by suggesting users with correct and related items. Today, abundant recommender systems have been developed for different fields and we put an effort on collaborative filtering (CF) recommender system. There are several problems in the recommender system such as Cold Start, Synonymy, Shilling Attacks, Privacy, Limited Content Analysis and Overspecialization, Grey Sheep, Sparsity, Scalability and Latency Problem. The current research explored the privacy in CF recommender system and defined the perspective privacy attributes (user's identity, password, address, and postcode/location) which are required to be addressed. Using the base models as Homomorphic and Hash Encryption scheme, we have proposed a hybrid model Homomorphic Hash Encryption (H2E) model that addressed the privacy issues according to defined objectives in the current study. Furthermore, in order to evaluate the privacy level, H2E was implementing in medicine recommender system and compared the consequences with existing state-of-the-art privacy protection mechanisms. It was observed that H2E outperform to other models with respect to determined privacy objectives. Leading to user's privacy, H2E can be considered a promising model for CF recommender systems.

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“…Identifying misleading information determines the news truthfulness by examining news content and related information, such as dissemination patterns [7]. Various perspectives have received much interest in addressing this issue, where fake news identification based on supervised learning dominates this domain and has succeeded.…”

Section: Introductionmentioning

confidence: 99%

Towards the Detection of Fake News on Social Networks Contributing to the Improvement of Trust and Transparency in Recommendation Systems: Trends and Challenges

2022

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In the age of the digital revolution and the widespread usage of social networks, the modalities of information consumption and production were disrupted by the shift to instantaneous transmission. Sometimes the scoop and exclusivity are just for a few minutes. Information spreads like wildfire throughout the world, with little regard for context or critical thought, resulting in the proliferation of fake news. As a result, it is preferable to have a system that allows consumers to obtain balanced news information. Some researchers attempted to detect false and authentic news using tagged data and had some success. Online social groups propagate digital false news or fake news material in the form of shares, reshares, and repostings. This work aims to detect fake news forms dispatched on social networks to enhance the quality of trust and transparency in the social network recommendation system. It provides an overview of traditional techniques used to detect fake news and modern approaches used for multiclassification using unlabeled data. Many researchers are focusing on detecting fake news, but fewer works highlight this detection’s role in improving the quality of trust in social network recommendation systems. In this research paper, we take an improved approach to assisting users in deciding which information to read by alerting them about the degree of inaccuracy of the news items they are seeing and recommending the many types of fake news that the material represents.

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Trust and Trustworthiness in Social Recommender Systems

Cited by 34 publications

References 14 publications

Incorporating AI and learning analytics to build trustworthy peer assessment systems

Incorporating AI and learning analytics to build trustworthy peer assessment systems

H2E: A Privacy Provisioning Framework for Collaborative Filtering Recommender System

Towards the Detection of Fake News on Social Networks Contributing to the Improvement of Trust and Transparency in Recommendation Systems: Trends and Challenges

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