کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6873239 | 1440631 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Collaborative QoS prediction with context-sensitive matrix factorization
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
How to obtain personalized quality of cloud/IoT services and assist users selecting the appropriate service has become a hot issue with the explosion of services on the Internet. Collaborative QoS prediction is proposed to address this issue by borrowing ideas from recommender systems. However, there is still a challenging problem as how to incorporate contextual factors into existing algorithms to realize context-aware QoS prediction as contextual factors play a crucial role in QoS assessment. In this paper, we propose a general context-sensitive matrix-factorization approach (CSMF) to make collaborative QoS prediction. By considering the complexity of service invocations, CSMF models the interactions of users-to-services and environment-to-environment simultaneously, and make full use of implicit and explicit contextual factors in the QoS data. Experimental results show that CSMF significantly outperforms the-state-of-art methods in metric of prediction accuracy. Particularly, when the QoS data is very sparse, CSMF is more effective and robust.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 82, May 2018, Pages 669-678
Journal: Future Generation Computer Systems - Volume 82, May 2018, Pages 669-678
نویسندگان
Hao Wu, Kun Yue, Bo Li, Binbin Zhang, Ching-Hsien Hsu,