A Web service QoS prediction approach based on time- and location-aware collaborative filtering

2014 ◽  
Vol 10 (2) ◽  
pp. 135-149 ◽  
Author(s):  
Chengyuan Yu ◽  
Linpeng Huang
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Qos Prediction ◽  
Location Aware ◽  
Prediction Approach

scholarly journals A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1556 ◽  
Author(s):  
Li Kuang ◽  
Long Yu ◽  
Lan Huang ◽  
Yin Wang ◽  
Pengju Ma ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Location Aware ◽  
Prediction Approach

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.

Location-aware and personalized collaborative filtering for web service recommendation

2016 ◽  
Vol 9 (5) ◽  
pp. 686-699 ◽  
Author(s):  
Jianxun Liu ◽  
Mingdong Tang ◽  
Zibin Zheng ◽  
Xiaoqing Liu ◽  
Saixia Lyu
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Service Recommendation ◽  
Location Aware

A location-aware matrix factorisation approach for collaborative web service QoS prediction

2019 ◽  
Vol 19 (3) ◽  
pp. 354
Author(s):  
Feng Li ◽  
Dianlong You ◽  
Chuan Ma ◽  
Limin Shen ◽  
Zhen Chen
Keyword(s):  
Web Service ◽  
Qos Prediction ◽  
Location Aware ◽  
Matrix Factorisation

A location-aware hybrid web service QoS prediction algorithm

2014 ◽  
Vol 21 ◽  
pp. 34-40 ◽  
Author(s):  
Hai-hong E ◽  
Jun-jie TONG ◽  
Mei-na SONG ◽  
Jun-de SONG
Keyword(s):  
Web Service ◽  
Prediction Algorithm ◽  
Qos Prediction ◽  
Location 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
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