Randomized latent factor model for high-dimensional and sparse matrices from industrial applications

Shang, Mingsheng; Luo, Xin; Liu, Zhigang; Chen, Jia; Yuan, Ye; Zhou, MengChu

doi:10.1109/jas.2018.7511189

Cited by 111 publications

(22 citation statements)

References 40 publications

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“…In the following figure 11, it can be observed that the age group (31)(32)(33)(34)(35)(36)(37)(38)(39)(40) has the highest frequency ratio as compared to other defined age groups. Furthermore, defined age groups are classified based on rental user gender, i.e., male and female.…”

Section: ) Rental Book Analysis Based On Rental User Age Groupmentioning

confidence: 93%

“…The following figure 12 presents the percentage (%) analysis of rental books based on defined user groups. It is evident that the age group (31)(32)(33)(34)(35)(36)(37)(38)(39)(40) has the highest percentage (%) value of 33.71% as compared to all other defined age groups. It can also be observed that the age group (41)(42)(43)(44)(45)(46)(47)(48)(49)(50) has the second-highest rental frequency percentage (%) value of 22.34%.…”

Section: ) Rental Book Analysis Based On Rental User Age Groupmentioning

confidence: 94%

“…Data mining techniques are widely used to analyze and extract hidden relationships and trends from a large amount of data. In [34], the authors proposed a latent factor based model to extract hidden knowledge from high dimensional and sparse matrices. According to the study [35], every organization with a large volume of data provides a platform to utilized data mining techniques to discover hidden knowledge to enhance work progress.…”

Section: Literature Reviewmentioning

confidence: 99%

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Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

et al. 2020

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Large scale data and predictive analytics are the most challenging tasks in the field of academic data mining. Academic libraries are a great source of information and knowledge to provide a wide range of services to meet end-user requirements. Due to the rapid changes in the educational environment and availability of huge library rental book data, it is required to utilize data mining and machine learning techniques in the context of the academic library to extract and analyze underlying knowledge from rental book data, which is important to facilitate library administration to drive better future decisions to improve and manage library resources effectively. These are the following resources, such as managing future demands of the library books, selection and arrangement of the books, operational efficiency, and also improve the quality of interaction between the library and end-users, etc. This work uses and analyzes a real dataset collected from the library of Jeju National University, the Republic of Korea. The dataset contains 2,211,413 rental book records including 173671 unique book records, 57203 unique number of the rental user, and 78 data parameters. In this paper, we propose a novel model to analyze and predict library rental book data to facilitates library administration in order to plan and manage library resources effectively and provide better services to end-users. The proposed model consists of two different modules; library data analysis and prediction modules. Firstly, we use data mining techniques to analyze and extract useful underlying patterns from library rental book data, which can lead to plan and manage library resources effectively. Secondly, a novel prediction model is proposed based on Deep Neural Network (DNN), Support Vector Regressor (SVR), and Random Forest (RF) to predict future usage of the academic libraries rental books. The performance results of the implemented regression models are evaluated in terms of MAE, MSE, and RMSE. In this paper, it is found that the DNN model performs significantly better than SVR and RF. The experimentation results show that the proposed model improves the future usage of library books to facilitate library administration to plan and manage library resources effectively. Based on the proposed model results, the academic library administration can easily plan and manage resources effectively to provide quality services to end-users. INDEX TERMS Data mining, academic libraries, big data, machine learning, predictive analysis.

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Section: ) Rental Book Analysis Based On Rental User Age Groupmentioning

confidence: 93%

Section: ) Rental Book Analysis Based On Rental User Age Groupmentioning

confidence: 94%

Section: Literature Reviewmentioning

confidence: 99%

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Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

et al. 2020

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“…The machine learning models are usually very large-scale, having a large number of parameters. The training of these models require searching very high-dimensional spaces, and conventional metaheuristic algorithm does not scale well to such high-dimensions [54].…”

Section: (A)mentioning

confidence: 99%

BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

Khan

Cao

et al. 2020

IEEE/CAA J. Autom. Sinica

168

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In this paper, we propose enhancements to Beetle Antennae Search (BAS) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the Adaptive Moment Estimation (ADAM) update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with Particle Swarm Optimizer (PSO) and the original BAS algorithm.

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“…In 2016, Dawar et al [20] presented the UFH algorithm to exploit HUIs efficiently on the sparse dataset. Moreover, on the sparse dataset, some other techniques also presented to discovery useful knowledge such as randomized latent factor model [21], distributed alternative stochastic gradient descent model DASGD [22] or model based on SGD extensions [23]. Recently, Nguyen et al [24] proposed an algorithm that relies on a new tight database format named MEFIM (Modified EFficient high-utility Itemset Mining), which can discover the desired itemsets efficiently.…”

Section: Introductionmentioning

confidence: 99%

Mining Correlated High Utility Itemsets in One Phase

et al. 2020

IEEE Access

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High-utility itemset mining (HUIM) in transaction databases has been extensively studied to discover interesting itemsets from users' purchase behaviors. With this, business managers can adjust their sale strategies appropriately to increase profit. HUIM approaches usually focus on the utility values of itemsets, but rarely evaluate the correlation of items in itemsets. Many high-utility itemsets are weakly correlated and have no real meaning. To address this issue, we suggest an algorithm, called CoHUI-Miner, to efficiently find correlated high-utility itemsets. In the proposed algorithm, we use the database projection mechanism to reduce the database size and present a new concept, called the prefix utility of projected transactions, to eliminate itemsets which do not satisfy the minimum threshold in the mining process. Experimental evaluation on two types of datasets from sparse to dense ones shows that CoHUI-Miner can efficiently mine correlated high-utility itemsets with regard to both execution time and memory usage. INDEX TERMS Correlated high-utility itemset, data mining, high-utility pattern, projected database.

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Randomized latent factor model for high-dimensional and sparse matrices from industrial applications

Cited by 111 publications

References 40 publications

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

Mining Correlated High Utility Itemsets in One Phase

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