Paper Info
Title
An On-Demand Service Aggregation And Service Recommendation Method Based On Rgps
Abstract
"Internet plus" application service recommendation is challenged by two issues: One is the increase in service volume and the disorderliness of the service organizations. A second is the diversification of user requirements. The research focus of this study was to investigate how to achieve more ordered aggregation and recommend services that meet the individualized requirements of users. This paper addresses the disorderliness of conventional service aggregation and considers the aggregation requirements of QoS weights with non-functional targets. Based on semantic relevance using the role (R), goal (G), process (P), service (S) demand metamodel, an RGPS association is proposed that is a weighted network for ordered QoS service aggregation. An individualized service recommendation method then is provided, based on an LSTM neural network with role and target backstepping using RGPS association network, that can achieve a high-quality precision service. Finally, a simulation experiment was carried out on service recommendations in the tourism domain, which verified the precision, effectiveness and application value of the service recommendation method.
Year
DOI
Venue
2019
10.3233/IDA-192628
INTELLIGENT DATA ANALYSIS
Keywords
Field
DocType
RGPS demand metamodel, RGPS association network, nonfunctional target requirement, LSTM neural network, service recommendation
On demand,Computer science,Artificial intelligence,Multimedia,Machine learning
Journal
Volume
Issue
ISSN
23
S1
1088-467X
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Yi Zhao110.70
Junfei Guo273.01
Ke-Qing He342863.80