Data Mining Based Recommendation System Using Social Websites

Faryal, Ali; Ahmad, Tauqir; Martinez-Enriquez, A. M.; Aslam, Muhammad

doi:10.1109/wi-iat.2015.78

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…After classification of data, the recommendation system (RS) ( Nagarnaik & Thomas, 2015 ; Sharma & Gera, 2013 ) uses data mining rules for providing recommendations using state of the art recommendation methods; collaborative filtering (CF), content-based filtering (CBF) or hybrid model ( Thorat, Goudar & Barve, 2015 ). It also uses data mining methods to provide recommendation on social network ( Najafabadi, Mohamed & Mahrin, 2019 ; Faryal et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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Data mining-based recommendation system using social networks—an analytical study

Ajmal

Awais

Khurshid

et al. 2023

PeerJ Computer Science

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In the current age, social media is commonly used and shares enormous data. However, a huge amount of data makes it difficult to deal with. It requires a lot of storage and processing time. The content produced by social media needs to be stored efficiently by using data mining methods for providing suitable recommendations. The goal of the study is to perform a systematic literature review (SLR) which finds, analyzes, and evaluates studies that relate to data mining-based recommendation systems using social networks (DRSN) from 2011 to 2021 and open up a path for scientific investigations to enhance the development of recommendation systems in a social network. The SLR follows Kitchenhem’s methodology for planning, guiding, and reporting the review. A systematic study selection procedure results in 42 studies that are analyzed in this article. The selected articles are examined on the base of four research questions. The research questions focus on publication venues, and chronological, and geographical distribution in DRSN. It also deals with approaches used to formulate DRSN, along with the dataset, size of the dataset, and evaluation metrics that validate the result of the selected study. Lastly, the limitations of the 42 studies are discussed. As a result, most articles published in 2018 acquired 21% of 42 articles, Whereas, China contributes 40% in this domain by comparing to other countries. Furthermore, 61% of articles are published in IEEE. Moreover, approximately 21% (nine out of 42 studies) use collaborative filtering for providing recommendations. Furthermore, the Twitter data set is common in that 19% of all other data sets are used, and precision and recall both cover 28% of selected articles for providing recommendations in social networks. The limitations show a need for a hybrid model that concatenates different algorithms and methods for providing recommendations. The study concludes that hybrid models may help to provide suitable recommendations on social media using data mining rules.

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Section: Introductionmentioning

confidence: 99%

“…The methods and techniques used for data mining based RS using social networks (DRSN) are large in number ( Najafabadi, Mohamed & Mahrin, 2019 ; Faryal et al, 2015 ). It is difficult to identify which methods can provide recommendations by using social websites.…”

Section: Introductionmentioning

confidence: 99%

Data mining-based recommendation system using social networks—an analytical study

Ajmal

Awais

Khurshid

et al. 2023

PeerJ Computer Science

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“…hidden inside, arises and has been used in a wide variety of applications, such as computer vision [1], telecommunication [2], and recommendation systems [3]- [6]. Nevertheless, traditional ML algorithms become computationally inefficient and fail to scale up well as the dimension of data grows.…”

Section: Introductionmentioning

confidence: 99%

Dual Optimization for Kolmogorov Model Learning Using Enhanced Gradient Descent

Duan,

Ghauch,

Kim

2021

Preprint

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Data representation techniques have made a substantial contribution to advancing data processing and machine learning (ML). Improving predictive power was the focus of previous representation techniques, which unfortunately perform rather poorly on the interpretability in terms of extracting underlying insights of the data. Recently, Kolmogorov model (KM) was studied, which is an interpretable and predictable representation approach to learning the underlying probabilistic structure of a set of random variables. The existing KM learning algorithms using semi-definite relaxation with randomization (SDRwR) or discrete monotonic optimization (DMO) have, however, limited utility to big data applications because they do not scale well computationally. In this paper, we propose a computationally scalable KM learning algorithm, based on the regularized dual optimization combined with enhanced gradient descent (GD) method. To make our method more scalable to largedimensional problems, we propose two acceleration schemes, namely, eigenvalue decomposition (EVD) elimination strategy and proximal EVD algorithm. Furthermore, a thresholding technique by exploiting the approximation error analysis and leveraging the normalized Minkowski 1-norm and its bounds, is provided for the selection of the number of iterations of the proximal EVD algorithm. When applied to big data applications, it is demonstrated that the proposed method can achieve compatible training/prediction performance with significantly reduced computational complexity; roughly two orders of magnitude improvement in terms of the time overhead, compared to the existing KM learning algorithms. Furthermore, it is shown that the accuracy of logical relation mining for interpretability by using the proposed KM learning algorithm exceeds 80%.

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Low-Rank Transfer Learning for Multi-stream Data Classification

Sun

Xie

et al. 2021

2021 11th International Conference on Information Science and Technology (ICIST)

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Data Mining Based Recommendation System Using Social Websites

Cited by 9 publications

References 15 publications

Data mining-based recommendation system using social networks—an analytical study

Data mining-based recommendation system using social networks—an analytical study

Dual Optimization for Kolmogorov Model Learning Using Enhanced Gradient Descent

Low-Rank Transfer Learning for Multi-stream Data Classification

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