Pattern2Vec: Representation of clickstream data sequences for learning user navigational behavior

Olmezogullari, Erdi; Aktaş, Mehmet S.

doi:10.1002/cpe.6546

Cited by 39 publications

(17 citation statements)

References 27 publications

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“…There exist studies that focus on analyzing the quality of the software [45,46]. However, in this study, we leave out the software quality analysis for future work.There are studies that focus on understanding users' actions by analyzing the user-system interaction data [47][48][49]. This study analyzes the users' behaviors based on their psychometric test data.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Risk Prediction Based on Psychometric Data

Duman,

Aktas,

Yahsi

2023

Computers

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In today’s financial landscape, traditional banking institutions rely extensively on customers’ historical financial data to evaluate their eligibility for loan approvals. While these decision support systems offer predictive accuracy for established customers, they overlook a crucial demographic: individuals without a financial history. To address this gap, our study presents a methodology for a decision support system that is intended to assist in determining credit risk. Rather than solely focusing on past financial records, our methodology assesses customer credibility by generating credit risk scores derived from psychometric test results. Utilizing machine learning algorithms, we model customer credibility through multidimensional metrics such as character traits and attitudes toward money management. Preliminary results from our prototype testing indicate that this innovative approach holds promise for accurate risk assessment.

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Section: Literature Reviewmentioning

confidence: 99%

Credit Risk Prediction Based on Psychometric Data

Duman,

Aktas,

Yahsi

2023

Computers

Self Cite

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“…There are studies that focus on understanding the user behaviors by analyzing user actions in different domains [28,31,34,35,38]. Also, some studies investigate the software quality [32,33].…”

Section: Literature Surveymentioning

confidence: 99%

A Framework for In-Car Loading Optimization Problem

Avcı,

Özbey,

Özen

et al. 2023

OPRD

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Speed is a crucial evaluation criterion in the cargo transportation industry. The processes carried out during transfers before cargo reaches the destination unit are vital for timely delivery. Time spent at cargo distribution centers should be minimized, the maximum number of packages should be transported in a single action between transfers, and cargo vehicles should be used as accurately and efficiently as possible. Minimizing unused space inside the vehicles and ensuring that vehicles at cargo transfer centers are arranged correctly and efficiently for faster departures and increased cargo capacity are of great importance. In pursuit of this goal, the present research concentrates on optimizing the loading of vehicle interiors. This investigation proposes a method that determines the optimal dimensions of packages to be loaded within the vehicle, with the intention of maximizing the number of cargo packages while minimizing the interior volume used. A preliminary implementation of the suggested approach has been created. This developed prototype application can assist cargo transportation companies in planning their cargo loading and optimizing the utilization of vehicle interiors. The efficacy of the prototype application was evaluated using a representative vehicle loading dataset, and the results demonstrate its success.

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“…Unlike these studies, we focus on machine learning workflows for frequent itemsets mining. There exists some studies that focus on using association rule mining 41,44 and sequential pattern mining libraries [56][57][58] of big data processing frameworks for different purposes. Here, we only focus on utilizing association rule mining and sequential pattern mining algorithms for recommendation systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Scalable recommendation systems based on finding similar items and sequences

Uzun‐Per

Gurel²,

Can³

et al. 2022

Concurrency and Computation

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The rapid growth in the airline industry, which started in 2009, continued until the COVID-19 era, with the annual number of passengers almost doubling in 10 years. This situation has led to increased competition between airline companies, whose profitability has decreased considerably. They aimed to increase their profitability by making services like seat selection, excess baggage, Wi-Fi access optional under the name of ancillary services. To the best of our knowledge, there is no recommendation system for recommending ancillary services for airline companies. Also, to the best of our knowledge, there is no testing framework to compare recommendation algorithms considering their scalabilities and running times. In this paper, we propose a framework based on Lambda architecture for recommendation systems that run on a big data processing platform. The proposed method utilizes association rule and sequential pattern mining algorithms that are designed for big data processing platforms. To facilitate testing of the proposed method, we implement a prototype application. We conduct an experimental study on the prototype to investigate the performance of the proposed methodology using accuracy, scalability, and latency related performance metrics. The results indicate that the proposed method proves to be useful and has negligible processing overheads.

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Pattern2Vec: Representation of clickstream data sequences for learning user navigational behavior

Cited by 39 publications

References 27 publications

Credit Risk Prediction Based on Psychometric Data

Credit Risk Prediction Based on Psychometric Data

A Framework for In-Car Loading Optimization Problem

Scalable recommendation systems based on finding similar items and sequences

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