Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

2020 ◽  
Vol 32 (9) ◽  
Author(s):  
Mayssa Fayala ◽  
Haithem Mezni
Keyword(s):  
Web Service ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Time Aware

A Spatial-Temporal QoS Prediction Approach for Time-aware Web Service Recommendation

2016 ◽  
Vol 10 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Xinyu Wang ◽  
Jianke Zhu ◽  
Zibin Zheng ◽  
Wenjie Song ◽  
Yuanhong Shen ◽  
...  
Keyword(s):  
Web Service ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Prediction Approach ◽  
Time Aware

QoS Prediction for Web Service Recommendation with Network Location-Aware Neighbor Selection

2016 ◽  
Vol 26 (04) ◽  
pp. 611-632 ◽  
Author(s):  
Yuyu Yin ◽  
Song Aihua ◽  
Gao Min ◽  
Xu Yueshen ◽  
Wang Shuoping
Keyword(s):  
Web Service ◽  
Real World ◽  
Missing Values ◽  
Prediction Models ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Network Location ◽  
Collaborative Recommendation ◽  
Location Aware ◽  
Neighbor Selection

Web service recommendation is one of the key problems in service computing, especially in the case of a large number of service candidates. The QoS (quality of service) values are usually leveraged to recommend services that best satisfy a user’s demand. There are many existing methods using collaborative filtering (CF) to predict QoS missing values, but very limited works can leverage the network location information in the user side and service side. In real-world service invocation scenario, the network location of a user or a service makes great impact on QoS. In this paper, we propose a novel collaborative recommendation framework containing three novel prediction models, which are based on two techniques, i.e. matrix factorization (MF) and network location-aware neighbor selection. We first propose two individual models that have the capability of using the user and service information, respectively. Then we propose a unified model that combines the results of the two individual models. We conduct sufficient experiments on a real-world dataset. The experimental results demonstrate that our models achieve higher prediction accuracy than baseline models, and are not sensitive to the parameters.

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