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Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education
المؤلفون المشاركون
Liu, Hanwen
Yan, Chao
Xu, Yanwei
Chen, Xuening
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-06-20
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Service recommendation has become one of the most effective approaches to quickly extract insightful information from big educational data.
However, the sparsity of educational service quality data (from multiple platforms or parties) used to make service recommendations often leads to few even null recommended results.
Moreover, to protect sensitive business information and obey laws, preserving user privacy during the abovementioned multisource data integration process is a very important but challenging requirement.
Considering the above challenges, this paper integrates Locality-Sensitive Hashing (LSH) with hybrid Collaborative Filtering (HCF) techniques for robust and privacy-aware data sharing between different platforms involved in the cross-platform service recommendation process.
Furthermore, to minimize the “False negative” recommended results incurred by LSH and enhance the success of recommended results, we propose two optimization strategies to reduce the probability that similar neighbours of a target user or similar services of a target service are overlooked by mistake.
Finally, we conduct a set of experiments based on a real distributed service quality dataset, i.e., WS-DREAM, to validate the feasibility and advantages of our proposed recommendation approach.
The extensive experimental results show that our proposal performs better than three competitive methods in terms of efficiency, accuracy, and successful rate while guaranteeing privacy-preservation.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chen, Xuening& Liu, Hanwen& Xu, Yanwei& Yan, Chao. 2019. Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education. Wireless Communications and Mobile Computing،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1212033
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chen, Xuening…[et al.]. Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education. Wireless Communications and Mobile Computing No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1212033
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chen, Xuening& Liu, Hanwen& Xu, Yanwei& Yan, Chao. Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education. Wireless Communications and Mobile Computing. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1212033
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1212033
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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