A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction
Author:
Affiliation:
1. College of Computer and Information Science, Southwest University, Chongqing, China
2. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
3. Old Dominion University, Norfolk, VA, USA
Funder
National Natural Science Foundation of China
Natural Science Foundation of Chongqing
CAS
Guangdong Province Universities and College Pearl River Scholar Funded Scheme
Key Cooperation Project of Chongqing Municipal Education Commission
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture
Link
http://xplorestaging.ieee.org/ielx7/4629386/10097430/09783168.pdf?arnumber=9783168
Reference50 articles.
1. Collaborative Web Service Quality Prediction via Exploiting Matrix Factorization and Network Map
2. Your neighbors alleviate cold-start: On geographical neighborhood influence to collaborative web service QoS prediction
3. QoS-Based Concurrent User-Service Grouping for Web Service Recommendation
4. Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization
5. Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models
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