A Recommender System Based on a Machine Learning Algorithm for B2C Portals

2009 ◽  
Author(s):  
L. M. Lopez-Lopez ◽  
J. J. Castro-Schez ◽  
D. Vallejo-Fernandez ◽  
J. Albusac
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals Developing a Scalable and Accurate Job Recommendation System with Distributed Cluster System using Machine Learning Algorithm

2021 ◽  
Vol 7 (2) ◽  
pp. 71-78
Author(s):  
Timothy Dicky ◽  
Alva Erwin ◽  
Heru Purnomo Ipung
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Hadoop Mapreduce ◽  
Job Recommendation ◽  
Hadoop Cluster ◽  
Time Required ◽  
Distributed Cluster

The purpose of this research is to develop a job recommender system based on the Hadoop MapReduce framework to achieve scalability of the system when it processes big data. Also, a machine learning algorithm is implemented inside the job recommender to produce an accurate job recommendation. The project begins by collecting sample data to build an accurate job recommender system with a centralized program architecture. Then a job recommender with a distributed system program architecture is implemented using Hadoop MapReduce which then deployed to a Hadoop cluster. After the implementation, both systems are tested using a large number of applicants and job data, with the time required for the program to compute the data is recorded to be analyzed. Based on the experiments, we conclude that the recommender produces the most accurate result when the cosine similarity measure is used inside the algorithm. Also, the centralized job recommender system is able to process the data faster compared to the distributed cluster job recommender system. But as the size of the data grows, the centralized system eventually will lack the capacity to process the data, while the distributed cluster job recommender is able to scale according to the size of the data.

Improving the Shilling Attack Detection in Recommender Systems Using an SVM Gaussian Mixture Model

2019 ◽  
Vol 18 (01) ◽  
pp. 1950011 ◽  
Author(s):  
Jasem M. Alostad
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Recommender System ◽  
Detection Rate ◽  
Learning Algorithm ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Machine Learning Algorithm

With recent advances in e-commerce platforms, the information overload has grown due to increasing number of users, rapid generation of data and items in the recommender system. This tends to create serious problems in such recommender systems. The increasing features in recommender systems pose some new challenges due to poor resilience to mitigate against vulnerable attacks. In particular, the recommender systems are more prone to be attacked by shilling attacks, which creates more vulnerability. A recommender system with poor detection of attacks leads to a reduced detection rate. The performance of the recommender system is thus affected with poor detection ability. Hence, in this paper, we improve the resilience against shilling attacks using a modified Support Vector Machine (SVM) and a machine learning algorithm. The Gaussian Mixture Model is used as a machine learning algorithm to increase the detection rate and it further reduces the dimensionality of data in recommender systems. The proposed method is evaluated against several result metrics, such as the recall rate, precision rate and false positive rate between different attacks. The results of the proposed system are evaluated against probabilistic recommender approaches to demonstrate the efficacy of machine learning language in recommender systems.

scholarly journals The Machine Learning Algorithm for Solving the Problem of Generating Recommendations for Goods and Services

2020 ◽  
Vol 10 (4) ◽  
pp. 5-16
Author(s):  
V.A. Sudakov ◽  
I.A. Trofimov
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Goods And Services ◽  
Complex Models ◽  
Combined Algorithm ◽  
Content Based Filtering

The article proposes an unsupervised machine learning algorithm for assessing the most possible relationship between two elements of a set of customers and goods / services in order to build a recommendation system. Methods based on collaborative filtering and content-based filtering are considered. A combined algorithm for identifying relationships on sets has been developed, which combines the advantages of the analyzed approaches. The complexity of the algorithm is estimated. Recommendations are given on the efficient implementation of the algorithm in order to reduce the amount of memory used. Using the book recommendation problem as an example, the application of this combined algorithm is shown. This algorithm can be used for a “cold start” of a recommender system, when there are no labeled quality samples of training more complex models.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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