Survival-Based Feature Extraction—Application in Supply Management for Dispersed Vending Machines

Grzegorowski, Marek; Litwin, Jarosław; Wnuk, Mateusz; Pabis, M.; Marcinowski, Lukasz

doi:10.1109/tii.2022.3178547

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…In our experiments, we used four datasets, namely: Movie-Lens 100K, MovieLens 1M 1 , MovieLens + IMDb/Rotten Tomatoes (abbreviated as HetRec-MovieLens) 2 , and LDOS-CoMoDa 3 . MovieLens datasets were collected by the Grou-pLens research project at the University of Minnesota.…”

Section: A Datasets and Evaluation Measuresmentioning

confidence: 99%

“…Modern businesses reach outstanding amounts of customers, who are often overwhelmed by the plethora of products and services [1], [2]. Online stores and content services attempt to provide a personalized shopping experience, tailoring their offerings to the specific interests of each client [1]- [3]. Viable solutions to this challenge are recommender systems (RS), which leverage the data collected to provide users with the most appealing products or services they are looking for [4].…”

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confidence: 99%

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Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Kannout,

Grzegorowski,

Grodzki

et al. 2024

IEEE Access

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Recommender systems (RS) are substantial for online shopping or digital content services. However, due to some data characteristics or insufficient historical data, may encounter considerable difficulties impacting the quality of their recommendations. This study introduces the clustering-based frequent pattern mining framework for recommender systems (Clustering-based FPRS) -a novel RS constituting several recommendation strategies leveraging agglomerative clustering and FP-growth algorithms. The developed strategies combine the generated frequent itemsets with collaborative-and content-filtering methods to address the cold-start problem, which occurs whenever a new user or item enters the system. In such cases, the RS has limited information about the new user or object. Thus, the recommendations may be inaccurate. The experimental evaluation on several benchmark datasets showed that Clustering-based FPRS is superior to state-of-the-art and could effectively alleviate the cold-start problem.INDEX TERMS Cold-start problem, recommender system, frequent pattern mining, clustering I. INTRODUCTION

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Section: A Datasets and Evaluation Measuresmentioning

confidence: 99%

mentioning

confidence: 99%

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Kannout,

Grzegorowski,

Grodzki

et al. 2024

IEEE Access

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“…Due to the general availability and affordability of cloud services [15], and the proven effectiveness of machine learning [5], [17], modern enterprises massively automate their processes and optimize decision-making with intelligent use of the collected data [3]. This trend is beneficial to many industries, including supply management and logistics [20].…”

Section: Related Workmentioning

confidence: 99%

“…Therefore we plan to further extend the developed FE methods by introducing histogram-based feature engineering [29]. Among other relevant, recently reported approaches that could be in the future implemented in our ML framework, we may indicate embedding selected statistics from survival analysis or features derived from deep learning methods into data representation [5], [30].…”

Section: Solution Overviewmentioning

confidence: 99%

“…M ACHINE learning (ML) algorithms are widely used within decision-support [1] or recommender systems [2] in many branches of the industry, like fast-moving consumer goods (FMCG) [3], e-commerce [4], logistics [5], or even hard-coal mining [6]. However, as ML models continue to run in production environments for an extended period, new expectations and concerns have also arisen.…”

Section: Introductionmentioning

confidence: 99%

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Considering various aspects of models’ quality in the ML pipeline - application in the logistics sector

Kannout¹,

Grodzki²,

Grzegorowski³

2022

Annals of Computer Science and Information Systems

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The industrial machine learning applications today involve developing and deploying MLOps pipelines to ensure the versatile quality of forecasting models over an extended period, simultaneously assuring the model's accuracy, stability, short training time, and resilience. In this study, we present the ML pipeline conforming to all the abovementioned aspects of models' quality formulated as a constrained multi-objective optimization problem. We also provide the reference implementation on stateof-the-art methods for data preprocessing, feature extraction, dimensionality reduction, feature and instance selection, model fitting, and ensemble blending. The experimental study on the real data set from the logistics industry confirmed the qualities of the proposed approach, as the successful participation in an international data competition did.

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Toward Recommender Systems Scalability and Efficacy

Kannout

Grzegorowski

Nguyen

2023

Studies in Computational Intelligence

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Survival-Based Feature Extraction—Application in Supply Management for Dispersed Vending Machines

Cited by 12 publications

References 24 publications

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Considering various aspects of models’ quality in the ML pipeline - application in the logistics sector

Toward Recommender Systems Scalability and Efficacy

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