An analytic approach to separate users by introducing new combinations of initial centers of clustering

Rashidi, Rahim; Khamforoosh, Keyhan; Sheikhahmadi, Amir

doi:10.1016/j.physa.2020.124185

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…According to the results in Tables (3)(4)(5), for all datasets and all training sizes, the proposed method has performed best in terms of MAE measures and, in some cases (6 out of 9), has performed best in terms of RMSE measures. Koren [1], and [35] demonstrated that small improvements in MAE or RMSE could have a significant impact on the quality of the top-few recommendations [16] and is not a trivial task.…”

Section: Evaluating the Performance For All Usersmentioning

confidence: 97%

“…Generally, in terms of approach, recommender systems can be classified into three categories: Content-Based (CB), Collaborative Filtering (CF), and hybrid approaches. The idea of CB methods is to learn users' profiles based on items' features (like genre and actors in movie-recommendation) [2] [3]. CF methods are based on the idea that similar users have similar tastes.…”

Section: Introductionmentioning

confidence: 99%

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How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution

Dadgar

Hamzeh

2022

IEEE Access

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With the increasing number of items in electronic retailers, news websites, etc., finding interesting items concerning the taste of users is becoming more challenging. Recommender Systems (RS) are a well-known solution to this issue. Collaborative filtering (CF) is a widely accepted and popular technique to implement an RS. However, cold-start and data sparsity problems reduce the performance of CF methods. One promising solution for these issues is to use the social trust information. However, how to properly use social trust information is a hot and still open question. In this paper, we propose a similarity measure and a simple link prediction method to address this question and employ them in trust-aware matrix factorization. Especially, our proposed similarity measure is asymmetric to consider the nature of social relationships. Also, to have a more accurate similarity estimation, we have considered both the user's historical ratings and trust relations, and we have determined the weight of each source. Finally, we have used the item-based model and the level of interest a user's trustee has for an item to improve the performance of the proposed method for sparse datasets. We conduct extensive performance evaluations in terms of rate prediction and interesting items found. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed method, especially in terms of Mean Absolute Error.

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Section: Evaluating the Performance For All Usersmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution

Dadgar

Hamzeh

2022

IEEE Access

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“…Data clustering methods place the data objects into groups, or clusters, such that objects in a given cluster tend to be similar to each other in some sense, and objects in different clusters tend to be dissimilar. These methods have various applications such as outlier detection [44], image segmentation [45], information retrieval [46], social networks [47], and System recommanders [48]. In the data privacy field, these techniques are useful to group data objects into at least k-member clusters that preserve privacy models such as λ-diversity.…”

Section: Data Clustering Schema By Partioning Algorithms On Apache Sparkmentioning

confidence: 99%

A distributed computing model for big data anonymization in the networks

Ashkouti¹,

Khamforoosh²

2023

PLoS ONE

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Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

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“…Therefore, how to effectively ensure the integrity and accuracy of the data and improve the data quality has become an urgent problem to be solved. As data collection and data expression methods become more and more diversified, it has become more convenient to obtain a large amount of multi-source heterogeneous data (Rashidi et al, 2020 ; Wu et al, 2020 ). The emergence of multi-source heterogeneous data and the need to mine the inherent information on such data naturally gave rise to modeling learning for multi-source heterogeneous data.…”

Section: Introductionmentioning

confidence: 99%

A Clustering Algorithm for Multi-Modal Heterogeneous Big Data With Abnormal Data

et al. 2021

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The problems of data abnormalities and missing data are puzzling the traditional multi-modal heterogeneous big data clustering. In order to solve this issue, a multi-view heterogeneous big data clustering algorithm based on improved Kmeans clustering is established in this paper. At first, for the big data which involve heterogeneous data, based on multi view data analyzing, we propose an advanced Kmeans algorithm on the base of multi view heterogeneous system to determine the similarity detection metrics. Then, a BP neural network method is used to predict the missing attribute values, complete the missing data and restore the big data structure in heterogeneous state. Last, we ulteriorly propose a data denoising algorithm to denoise the abnormal data. Based on the above methods, we construct a framework namely BPK-means to resolve the problems of data abnormalities and missing data. Our solution approach is evaluated through rigorous performance evaluation study. Compared with the original algorithm, both theoretical verification and experimental results show that the accuracy of the proposed method is greatly improved.

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An analytic approach to separate users by introducing new combinations of initial centers of clustering

Cited by 11 publications

References 22 publications

How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution

How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution

A distributed computing model for big data anonymization in the networks

A Clustering Algorithm for Multi-Modal Heterogeneous Big Data With Abnormal Data

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