The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning

Wang, Shoujin; Pasi, Gabriella; Hu, Liang; Cao, Longbing

doi:10.1109/mis.2020.3026430

Cited by 27 publications

(10 citation statements)

References 10 publications

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“…As part of the era of artificial intelligence (AI) and big data, specialized issues have been published to explore complex data, advanced AI, and combinations with RSs. A special issue [25] aimed to complement these discussions and inspire readers to tackle existing and emerging challenges in building RSs mostly focused on AI techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Quality of Life in Chronic Patients: A Pilot Study on the Effectiveness of a Health Recommender System and Its Usability

Río

Jimenez²,

Medina-García

et al. 2023

Applied Sciences

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The TeNDER project aims to improve the quality of life (QoL) of chronic patients through an integrated care ecosystem. This study evaluates the health recommender system (HRS) developed for the project, which offers personalized recommendations based on data collected from a set of monitoring devices. The list of notifications covered different areas of daily life such as physical activity, nutrition, and sleep. We conducted this case study to evaluate the effectiveness and usability of the HRS in providing accurate and relevant recommendations to users. Evaluation process consisted on survey administration for QoL assessment and the satisfaction and usability of the HRS. The four-week pilot study involved several patients and caregivers and demonstrated that the HRS was perceived as user-friendly, consistent, and helpful, with a positive impact on patients’ QoL. However, the study highlights the need for improvement in terms of personalization of recommendations.

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Section: Literature Reviewmentioning

confidence: 99%

Improving Quality of Life in Chronic Patients: A Pilot Study on the Effectiveness of a Health Recommender System and Its Usability

Río

Jimenez²,

Medina-García

et al. 2023

Applied Sciences

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“…The role of data mining techniques in this system is to analyze students' learning behaviors and discover potential patterns and trends [12]. For example, by analyzing historical data, the system can identify which course content has the greatest impact on student learning outcomes or which types of programs are more motivating and engaging [13]. Implementing such a recommender system requires consideration of several aspects.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Recommendation of Computer Course Items Combining Augmented Learning Algorithms and Data Mining Techniques

Zhao,

Jing,

Guo

2024

Applied Mathematics and Nonlinear Sciences

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This paper combines augmented learning algorithms and data mining techniques for an intelligent recommendation system for computer course projects to optimize the allocation of learning resources and improve learning efficiency and effectiveness. The research methodology includes an improved association rule mining algorithm and an augmented learning model applying deep Q-network and adaptive reward function. The research results show that the system can effectively mine knowledge point associations and achieve personalized recommendation. In particular, the accuracy of knowledge point associations and the customized level of recommendation are improved by multi-support strategy improvement and knowledge point association correction. Experiments show that on the edX dataset, the recommendation recall reaches 0.347 at k=10 and 0.433 at k=20, significantly better than the traditional recommendation algorithm. This intelligent recommendation system can effectively improve the learning effect of computer course projects, and it has a positive impact on both learning attitude and independent learning ability, especially helping the backward students significantly and reducing the learning differences between classes.

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“…However, traditional graph perturbation methods mainly anonymize graphs in high‐dimensional space, ignoring the fact that low‐dimensional representations can reveal private information. Researchers found that attackers can use GNN‐based, 2 deep learning based, 3 reinforcement learning based 4 or other corresponding approaches 5 to infer privacy information about certain nodes from low‐dimensional graph representations. For instance, in Figure 1A, a user chose to hide his attribute location and social relationships, which have been removed from the published anonymous graph.…”

Section: Introductionmentioning

confidence: 99%

Privacy‐preserving graph publishing with disentangled variational information bottleneck

Ma,

Li,

Sun

et al. 2023

Concurrency and Computation

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SummarySocial networks collect enormous amounts of user personal and behavioral data, which could threaten users' privacy if published or shared directly. Privacy‐preserving graph publishing (PPGP) can make user data available while protecting private information. For this purpose, in PPGP, anonymization methods like perturbation and generalization are commonly used. However, traditional anonymization methods are challenging in balancing high‐level privacy and utility, ineffective at defending against both various link and hybrid inference attacks, as well as vulnerable to graph neural network (GNN)‐based attacks. To solve those problems, we present a novel privacy‐disentangled approach that disentangles private and non‐private information for a better privacy‐utility trade‐off. Moreover, we propose a unified graph deep learning framework for PPGP, denoted privacy‐disentangled variational information bottleneck (PDVIB). Using low‐dimensional perturbations, the model generates an anonymized graph to defend against various inference attacks, including GNN‐based attacks. Particularly, the model fits various privacy settings by employing adjustable perturbations at the node level. With three real‐world datasets, PDVIB is demonstrated to generate robust anonymous graphs that defend against various privacy inference attacks while maintaining the utility of non‐private information.

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The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning

Cited by 27 publications

References 10 publications

Improving Quality of Life in Chronic Patients: A Pilot Study on the Effectiveness of a Health Recommender System and Its Usability

Improving Quality of Life in Chronic Patients: A Pilot Study on the Effectiveness of a Health Recommender System and Its Usability

Research on Intelligent Recommendation of Computer Course Items Combining Augmented Learning Algorithms and Data Mining Techniques

Privacy‐preserving graph publishing with disentangled variational information bottleneck

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