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.

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

The applying of machine learning methods to improve the quality of well casing

2020 ◽  
pp. 81-93
Author(s):  
D. V. Shalyapin ◽  
D. L. Bakirov ◽  
M. M. Fattakhov ◽  
A. D. Shalyapina ◽  
A. V. Melekhov ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
New Approach ◽  
Geological Conditions ◽  
Expert Assessments ◽  
The Impact ◽  
The Relationship

The article is devoted to the quality of well casing at the Pyakyakhinskoye oil and gas condensate field. The issue of improving the quality of well casing is associated with many problems, for example, a large amount of work on finding the relationship between laboratory studies and actual data from the field; the difficulty of finding logically determined relationships between the parameters and the final quality of well casing. The text gives valuable information on a new approach to assessing the impact of various parameters, based on a mathematical apparatus that excludes subjective expert assessments, which in the future will allow applying this method to deposits with different rock and geological conditions. We propose using the principles of mathematical processing of large data sets applying neural networks trained to predict the characteristics of the quality of well casing (continuity of contact of cement with the rock and with the casing). Taking into account the previously identified factors, we developed solutions to improve the tightness of the well casing and the adhesion of cement to the limiting surfaces.

scholarly journals Comparison of marker selection methods for high throughput scRNA-seq data

2019 ◽  
Author(s):  
Anna C. Gilbert ◽  
Alexander Vargo
Keyword(s):  
Performance Measures ◽  
Synthetic Data ◽  
Large Data ◽  
Ground Truth ◽  
Selection Method ◽  
Large Data Sets ◽  
Data Sets ◽  
Selection Methods ◽  
Marker Selection

AbstractHere, we evaluate the performance of a variety of marker selection methods on scRNA-seq UMI counts data. We test on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells. In addition, we propose several performance measures for evaluating the quality of a set of markers when there is no known ground truth. According to these metrics, most existing marker selection methods show similar performance on experimental scRNA-seq data; thus, the speed of the algorithm is the most important consid-eration for large data sets. With this in mind, we introduce RANKCORR, a fast marker selection method with strong mathematical underpinnings that takes a step towards sensible multi-class marker selection.

scholarly journals Qos Web Service Design Based on Collaborative Filtering

CONVERTER ◽  
2021 ◽  
pp. 107-115
Author(s):  
Yu-ping LI, Ke LI, Zhan-jie Guo
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Service Recommendation ◽  
Recommendation Algorithm ◽  
Similar Weight ◽  
Definition Of ◽  
Collaborative Prediction

In the process of web service recommendation, the prediction accuracy of Web Service missing Quality of Service (QoS) value will have an important impact on the rationality of service recommendation. Therefore, combined with spatiotemporal similarity perception, this paper proposes a new web service QoS collaborative filtering recommendation algorithm. This paper designs the framework of web service recommendation system from the perspective of QoS collaborative prediction, and gives the definition of related parameter set. Aiming at the problem that some services in the traditional Top-k algorithm are not similar to the target services, the spatial-temporal similarity perception combined with similar weight is used to predict the missing data to improve the prediction accuracy. In this paper, the calculation process of the algorithm is given through a simple example. The effectiveness of the algorithm is verified by the experimental results.

The Performance of Location Aware Shilling Attacks in Web Service Recommendation

2017 ◽  
Vol 14 (3) ◽  
pp. 53-66 ◽  
Author(s):  
Min Gao ◽  
Xiang Li ◽  
Wenge Rong ◽  
Junhao Wen ◽  
Qingyu Xiong
Keyword(s):  
Web Service ◽  
Service Recommendation ◽  
Location Factors ◽  
Attack Model ◽  
Location Aware ◽  
Random Models ◽  
Successful Technique ◽  
The Impact ◽  
Attack Models

The location aware collaborative filtering (LACF) is one of the most successful technique of predicting the Quality of Service (QoS) in Internet of Things (IoT) service recommendation systems. However, the openness of CF web service recommendation renders them vulnerable to the injection of attack profiles consisting of apocryphal QoS values (also identified as shilling attacks). Combined with location factors, such profiles might exert greater impact on the LACF compared with traditional CF method. Unfortunately, to the best of the authors' knowledge, there is few research on such kind of attack model in the literature. Therefore, in this paper, the authors first construct three kinds of attack models including LAA, LAB, and LAR (location aware - average, bandwagon, and random) models and compare the impact of the classical shilling attacks (CSA) and location aware shilling attacks (LASA) on LACF. Furthermore, the authors use two attack detectors to compare the robustness of CSA and LASA. The experimental results on WS-DREAM dataset indicate that the LACF indeed suffers from CSA and LASA. Besides, in comparison with CSA, the LASA models do not always exert more influence on the LACF and the profiles injected by LASA are easier to be detected.

Application and Research on Distributed Collaborative Filtering Recommendation Algorithm Based on Hadoop

2015 ◽  
Vol 713-715 ◽  
pp. 1615-1621
Author(s):  
Xiu Juan Li ◽  
He Biao Yang
Keyword(s):  
Parallel Computing ◽  
Comparative Analysis ◽  
Collaborative Filtering ◽  
Computational Efficiency ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Recommendation Algorithm ◽  
Exponential Expansion ◽  
Hadoop Platform

Coupled with exponential expansion of the data, efficient computing of existing recommendation algorithm has become an important issue, and the traditional collaborative filtering recommendation algorithm also exist the problem of sparsity. Based on the detailed analysis, the article introduce Hadoop platform into improved collaborative filtering recommendation algorithm, the improved collaborative filtering recommendation algorithm solve the problem of data sparsity, MapReduce parallel computing of recommendation also solve the promble of computational efficiency. In the experiments, the comparative analysis between Hadoop platform implementation and the previous implementation draws the conclusion that the Hadoop platform improves collaborative filtering recommendation algorithm computation efficiently under conditions of large data sets.

Inherent Limitations of Hospital Death Rates to Assess Quality

1990 ◽  
Vol 6 (2) ◽  
pp. 220-228 ◽  
Author(s):  
Robert W. Dubois
Keyword(s):  
Death Rate ◽  
Large Data ◽  
Potential Method ◽  
Large Data Sets ◽  
Hospital Death ◽  
Data Sets ◽  
Death Rates ◽  
Rate Study ◽  
Assess Quality

AbstractModeling death rates has been suggested as a potential method to screen hospitals and identify superior and substandard providers. This article begins with a review of one hospital death rate study and focuses upon its findings and limitations. It also explores the inherent limitations in the use of large data sets to assess quality of care.

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