scholarly journals Robustness analysis of location aware collaborative filtering for web service recommendation

2016 ◽  
Author(s):  
Xiang Li ◽  
◽  
Min Gao ◽  
Wenge Rong ◽  
Junhao Wen ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Robustness Analysis ◽  
Service Recommendation ◽  
Location Aware

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

Location-Based Collaborative Filtering for Web Service Recommendation

2019 ◽  
Vol 12 (1) ◽  
pp. 34-40
Author(s):  
Mareeswari Venkatachalaappaswamy ◽  
Vijayan Ramaraj ◽  
Saranya Ravichandran
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Information Filtering ◽  
Service Selection ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Service Recommendation ◽  
Location Aware

Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items’ list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list. Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services. Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user’s interest by collecting taste information from many users. Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources. Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms.

Deep hybrid collaborative filtering for Web service recommendation

2018 ◽  
Vol 110 ◽  
pp. 191-205 ◽  
Author(s):  
Ruibin Xiong ◽  
Jian Wang ◽  
Neng Zhang ◽  
Yutao Ma
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Service Recommendation ◽  
Hybrid Collaborative Filtering

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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