A Lightweight Matrix Factorization for Recommendation With Local Differential Privacy in Big Data

Zhou, Hao; Yang, Geng; Xiang, Yang; Bai, Yunlu; Wang, Weiya

doi:10.1109/tbdata.2021.3139125

Cited by 22 publications

(2 citation statements)

References 27 publications

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“…The technique involves decomposing a matrix of user-item interactions into lower-dimensional matrices, and then using those matrices to predict missing entries and recommend new items. For example, in [104], the authors propose a lightweight matrix factorisation algorithm that reduces the number of parameters required for training, while maintaining the accuracy of the recommendations.…”

Section: Ai Techniques For the Ux Of Recommender Systemsmentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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Among the primary aims of Artificial Intelligence (AI) is the enhancement of User Experience (UX) by providing deep understanding, profound empathy, tailored assistance, useful recommendations, and natural communication with human interactants while they are achieving their goals through computer use. To this end, AI is used in varying techniques to automate sophisticated functions in UX and thereby changing what UX is apprehended by the users. This is achieved through the development of intelligent interactive systems such as virtual assistants, recommender systems, and intelligent tutoring systems. The changes are well received, as technological achievements but create new challenges of trust, explainability and usability to humans, which in turn need to be amended by further advancements of AI in reciprocity. AI can be utilised to enhance the UX of a system while the quality of the UX can influence the effectiveness of AI. The state of the art in AI for UX is constantly evolving, with a growing focus on designing transparent, explainable, and fair AI systems that prioritise user control and autonomy, protect user data privacy and security, and promote diversity and inclusivity in the design process. Staying up to date with the latest advancements and best practices in this field is crucial. This paper conducts a critical analysis of published academic works and research studies related to AI and UX, exploring their interrelationship and the cause-effect cycle between the two. Ultimately, best practices for achieving a successful interrelationship of AI in UX are identified and listed based on established methods or techniques that have been proven to be effective in previous research reviewed.

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Section: Ai Techniques For the Ux Of Recommender Systemsmentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

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“…In addition, data present in the health care database increases endlessly and intruders have high chance in extracting the information. In recent era, differential privacy (DP) [18] has attracted the researchers a lot by introducing k-anonymity and l-diversity and providing high data securing in data mining.…”

Section: Introductionmentioning

confidence: 99%

Improved 3D Rotation-based Geometric Data Perturbation Based on Medical Data Preservation in Big Data

Dansana¹,

Kabat²,

Pattnaik³

2023

IJACSA

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With the rise in technology, a huge volume of data is being processed using data mining, especially in the healthcare sector. Usually, medical data consist of a lot of personal data, and third parties utilize it for the data mining process. Perturbation in health care data highly aids in preventing intruders from utilizing the patient's privacy. One of the challenges in data perturbation is managing data utility and privacy protection. Medical data mining has certain special properties compared with other data mining fields. Hence, in this work, the machine learning (ML) based perturbation approach is introduced to provide more privacy to healthcare data. Here, clustering and IGDP-3DR processes are applied to improve healthcare privacy preservation. Initially, the dataset is pre-processed using data normalization. Then, the dimensionality is reduced by SVD with PCA (singular value decomposition with Principal component analysis). Then, the clustering process is performed by IFCM (Improved Fuzzy C means). The high-dimensional data are divided into several segments by IFCM, and every partition is set as a cluster. Then, improved Geometric Data Perturbation (IGDP) is used to perturb the clustered data. IGDP is a combination of GDP with 3D rotation (3DR). Finally, the perturbed data are classified using a machine learning (ML) classifier -kernel Support Vector Machine-Horse Herd Optimization (KSVM-HHO) to classify the perturbed data and ensure better accuracy. The overall evaluation of the proposed KSVM-HHO is carried out in the Python platform. The performance of the IGDP-KSVM-HHO is compared over the two benchmark datasets, Wisconsin prognostic breast cancer (WBC) and Pima Indians Diabetes (PID) dataset. For the WBC dataset, the proposed method obtains an overall accuracy of 98.08% perturbed data, and for the PID dataset, the proposed method obtains an overall accuracy of 98.04%.

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TSFed: A Two-Stage Federated Learning Framework via Cloud-Edge Collaboration for Services QoS Prediction

Jian

et al. 2022

Lecture Notes in Computer Science

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A Lightweight Matrix Factorization for Recommendation With Local Differential Privacy in Big Data

Cited by 22 publications

References 27 publications

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Improved 3D Rotation-based Geometric Data Perturbation Based on Medical Data Preservation in Big Data

TSFed: A Two-Stage Federated Learning Framework via Cloud-Edge Collaboration for Services QoS Prediction

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