scholarly journals A machine learning recommender system based on collaborative filtering using Gaussian mixture model clustering

2021 ◽  
Author(s):  
Delshad Mohammad Shakoor ◽  
Vafa Maihami ◽  
Reza Maihami
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Recommender System ◽  
Gaussian Mixture ◽  
Mixture Model Clustering

scholarly journals A Machine Learning Recommender System Based on Collaborative Filtering Using Gaussian Mixture Model Clustering

2020 ◽  
Author(s):  
Delshad Fakoor ◽  
Vafa Maihami ◽  
Reza Maihami
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Recommender System ◽  
Pearson Correlation ◽  
Gaussian Mixture ◽  
Similarity Criteria ◽  
Data Set ◽  
Mixture Model Clustering

Changing and moving toward online shopping has made it necessary to customize customers’ needs and provide them more selective options. The buyers search the products’ features before deciding to purchase items. The recommender systems facilitate the searching task for customers via narrowing down the search space within the specific products that align the customer needs. Clustering, as a typical machine learning approach, is applied in recommender systems. As an information filtering method, a recommender system clusters user’s data to indicate the required factors for more accurate predictions by calculating the similarity between members of a cluster. In this study, using the Gaussian mixture model clustering and considering the scores distance and the value of scores in the Pearson correlation coefficient, a new method is introduced for predicting scores in machine learning recommender systems. To study the proposed method’s performance, a Movie Lens data set is evaluated, and the results are compared to some other recommender systems, including the Pearson correlation coefficients similarity criteria, K-means, and fuzzy C-means algorithms. The simulation results indicate that our method has less error than others by increasing the number of neighbors. The results also illustrate that when the number of users increases, the proposed method’s accuracy will increase. The reason is that the Gaussian mixture clustering chooses similar users and considers the scores distance in choosing similar neighbors to the active user.

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.

Hierarchical prediction based on two-level Gaussian mixture model clustering for bike-sharing system

2019 ◽  
Vol 178 ◽  
pp. 84-97 ◽  
Author(s):  
Wenzhen Jia ◽  
Yanyan Tan ◽  
Li Liu ◽  
Jing Li ◽  
Huaxiang Zhang ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Bike Sharing ◽  
Mixture Model Clustering

Gaussian Mixture Model Clustering with Incomplete Data

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-14
Author(s):  
Yi Zhang ◽  
Miaomiao Li ◽  
Siwei Wang ◽  
Sisi Dai ◽  
Lei Luo ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Incomplete Data ◽  
Optimization Problem ◽  
Gaussian Mixture ◽  
Practical Applications ◽  
Learning Procedure ◽  
Benchmark Datasets ◽  
Effectiveness And Efficiency ◽  
Mixture Model Clustering

Gaussian mixture model (GMM) clustering has been extensively studied due to its effectiveness and efficiency. Though demonstrating promising performance in various applications, it cannot effectively address the absent features among data, which is not uncommon in practical applications. In this article, different from existing approaches that first impute the absence and then perform GMM clustering tasks on the imputed data, we propose to integrate the imputation and GMM clustering into a unified learning procedure. Specifically, the missing data is filled by the result of GMM clustering, and the imputed data is then taken for GMM clustering. These two steps alternatively negotiate with each other to achieve optimum. By this way, the imputed data can best serve for GMM clustering. A two-step alternative algorithm with proved convergence is carefully designed to solve the resultant optimization problem. Extensive experiments have been conducted on eight UCI benchmark datasets, and the results have validated the effectiveness of the proposed algorithm.

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