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Electronics
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Machine Learning in Recommender Systems and Prediction Model
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Machine Learning in Recommender Systems and Prediction Model

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 8219

Special Issue Editors

Prof. Dr. Jaekwang Kim

E-Mail Website
Guest Editor
College of Computing and Informatics, Sungkyunkwan University, Seoul 26111, Republic of Korea
Interests: artificial intelligence; recommendation & prediction system; intelligent system; machine intelligence & learning; pattern analysis; medical intelligence system
Prof. Dr. Hayoung Oh

E-Mail Website
Guest Editor
College of Computing and Informatics, Sungkyunkwan University, Seoul 26111, Republic of Korea
Interests: social & computer network analysis; recommender system; data analysis & artificial intelligence

Special Issue Information

Dear Colleagues,

A recommender system, or a recommendation system, is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. Recommender systems are utilized in a variety of areas but are most commonly recognized as playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms. Machine learning techniques play a central role in the development and improvement of recommender systems. Collaborative filtering and content-based filtering are the two main categories of recommendation algorithms, both of which can be implemented using various machine learning techniques such as neural networks and decision trees. These systems can operate using either explicit feedback, such as ratings or rankings, or implicit feedback, such as clicking on a link or making a purchase. Overall, the use of machine learning in recommender systems has led to significant improvements in the quality and diversity of recommendations, as well as increased user engagement and satisfaction.

We welcome authors to contribute with original or review manuscripts on advanced applications of MR in biomedical imaging and spectroscopy.

Topics of interest include, but are not limited to, the following areas:

  • The scalability, performance, and implementation of algorithms in recommender systems;
  • The bias, fairness, bubbles, and ethics of recommender systems;
  • Case studies of real-world applications of recommender systems;
  • Recommender systems that use conversational and natural language processing;
  • Cross-domain recommendations, or the use of recommender systems in different areas or industries;
  • The data characteristics and processing challenges that are unique to recommender systems;
  • Economic models and consequences of the use of recommender systems;
  • User interfaces for recommender systems;
  • Recommendations that consider multiple stakeholders or perspectives;
  • New methods for evaluating the effectiveness of recommender systems;
  • Innovative approaches to recommendation, including those using voice and virtual/augmented reality;
  • Techniques for eliciting user preferences;
  • Privacy and security considerations in recommender systems;
  • Recommender systems that are aware of social and contextual factors;
  • Challenges in building scalable, high-quality, and high-performing recommender systems;
  • Studies of how users interact with and experience recommendation applications.

Prof. Dr. Jaekwang Kim
Prof. Dr. Hayoung Oh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • recommender systems
  • prediction model
  • natural language processing

Published Papers (5 papers)

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Research

17 pages, 8663 KiB  
Article
From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems
by Zhixiong Jin and Byeongjoon Noh
Electronics 2023, 12(20), 4335; https://doi.org/10.3390/electronics12204335 - 19 Oct 2023
Cited by 1 | Viewed by 1211
Abstract
We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values in the dataset due to [...] Read more.
We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values in the dataset due to the rarity of traffic accidents. To address the issue, we propose a grid-clustered feature map with the ideas of grids and cells. To predict the occurrence of accidents in the grid, we introduce an accident detector that combines the power of a Convolutional Neural Network (CNN) with a Deep Neural Network (DNN). Then, hierarchical DNNs are supposed to be an accident risk classifier to estimate the risk of each cell in the accident-occurrence grid. The proposed system can effectively reduce instances with no traffic accidents. Furthermore, we introduce the concept of the Accident Risk Index (ARI) to better represent the severity of risk at each cell. Also, we consider all the explanatory variables, such as dangerous driving behaviors, traffic mobility, and safety facility information, that can be related to traffic accidents. To improve the prediction accuracy, we further take into consideration all the explanatory variables, such as dangerous driving behaviors, traffic mobility, and safety facility information, that can be related to traffic accidents. In the experiment, we highlight the benefits of our method for urban traffic accident management by significantly improving model performance compared to the baselines. The feasibility and applicability of the proposed system are validated in the data of Daejeon City, Republic of Korea. The proposed prediction system can dynamically advise and recommend commuters, traffic management systems, and city planners on alternatives, optimizations, and interventions. Full article
(This article belongs to the Special Issue Machine Learning in Recommender Systems and Prediction Model)
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25 pages, 2040 KiB  
Article
Car Price Quotes Driven by Data-Comprehensive Predictions Grounded in Deep Learning Techniques
by Andreea Dutulescu, Andy Catruna, Stefan Ruseti, Denis Iorga, Vladimir Ghita, Laurentiu-Marian Neagu and Mihai Dascalu
Electronics 2023, 12(14), 3083; https://doi.org/10.3390/electronics12143083 - 15 Jul 2023
Cited by 1 | Viewed by 2086
Abstract
The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various [...] Read more.
The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various machine learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to be studied. This research introduced two datasets from different markets, one with over 300,000 entries from Germany to serve as a training basis for deep prediction models and a second dataset from Romania containing more than 15,000 car quotes used mainly to observe local traits. As such, we included extensive cross-market analyses by comparing the emerging Romanian market versus one of the world’s largest and most developed car markets, Germany. Our study used several neural network architectures that captured complex relationships between car model features, individual add-ons, and visual features to predict used car prices accurately. Our models achieved a high R2 score exceeding 0.95 on both datasets, indicating their effectiveness in estimating used car prices. Moreover, we experimented with advanced convolutional architectures to predict car prices based solely on visual features extracted from car images. This approach exhibited transfer-learning capabilities, leading to improved prediction accuracy, especially since the Romanian training dataset was limited. Our experiments highlighted the most important factors influencing the price, while our findings have practical implications for buyers and sellers in assessing the value of vehicles. At the same time, the insights gained from this study enable informed decision making and provide valuable guidance in the used car market. Full article
(This article belongs to the Special Issue Machine Learning in Recommender Systems and Prediction Model)
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26 pages, 1919 KiB  
Article
Optimizing Neural Networks for Imbalanced Data
by I. de Zarzà, J. de Curtò and Carlos T. Calafate
Electronics 2023, 12(12), 2674; https://doi.org/10.3390/electronics12122674 - 14 Jun 2023
Cited by 1 | Viewed by 1915
Abstract
Imbalanced datasets pose pervasive challenges in numerous machine learning (ML) applications, notably in areas such as fraud detection, where fraudulent cases are vastly outnumbered by legitimate transactions. Conventional ML methods often grapple with such imbalances, resulting in models with suboptimal performance concerning the [...] Read more.
Imbalanced datasets pose pervasive challenges in numerous machine learning (ML) applications, notably in areas such as fraud detection, where fraudulent cases are vastly outnumbered by legitimate transactions. Conventional ML methods often grapple with such imbalances, resulting in models with suboptimal performance concerning the minority class. This study undertakes a thorough examination of strategies for optimizing supervised learning algorithms when confronted with imbalanced datasets, emphasizing resampling techniques. Initially, we explore multiple methodologies, encompassing Gaussian Naive Bayes, linear and quadratic discriminant analysis, K-nearest neighbors (K-NN), support vector machines (SVMs), decision trees, and multi-layer perceptron (MLP). We apply these on a four-class spiral dataset, a notoriously demanding non-linear classification problem, to gauge their effectiveness. Subsequently, we leverage the garnered insights for a real-world credit card fraud detection task on a public dataset, where we achieve a compelling accuracy of 99.937%. In this context, we compare and contrast the performances of undersampling, oversampling, and the synthetic minority oversampling technique (SMOTE). Our findings highlight the potency of resampling strategies in augmenting model performance on the minority class; in particular, oversampling techniques achieve the best performance, resulting in an accuracy of 99.928% with a significantly low number of false negatives (21/227,451). Full article
(This article belongs to the Special Issue Machine Learning in Recommender Systems and Prediction Model)
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14 pages, 539 KiB  
Article
A Spatio-Temporal Hybrid Neural Network for Crowd Flow Prediction in Key Urban Areas
by Du He, Jing Jia, Yaoqing Wang, Lan You, Zhijun Chen, Jiawen Li, Qiyao Wu and Yongsen Wang
Electronics 2023, 12(10), 2255; https://doi.org/10.3390/electronics12102255 - 16 May 2023
Viewed by 940
Abstract
The prediction of crowd flow in key urban areas is an important basis for city informatization development and management. Timely understanding of crowd flow trends can provide cities with data support in epidemic prevention, public security management, and other aspects. In this paper, [...] Read more.
The prediction of crowd flow in key urban areas is an important basis for city informatization development and management. Timely understanding of crowd flow trends can provide cities with data support in epidemic prevention, public security management, and other aspects. In this paper, the model uses the Node2Vec graph embedding algorithm combined with LSTM (NDV-LSTM) to predict crowd flow. The model first analyzes the correspondence between key areas and grid centers, and the Node2Vec graph embedding algorithm was used to extract spatial features. At the same time, considering urban region type, weather, temperature, and other crowd flow data features, the long short-term memory (LSTM) network model was used for unified modeling. The model uses the crowd flow of the previous three days to predict the crowd flow of the next day. The model was evaluated on the 2020 CCF crowd density competition data set. The experimental results show that the NDV-LSTM model can capture the features of the region association digraph and various crowd flow correlation factors well, and the mean square error of the prediction of the crowd flow in key areas is reduced to 1.5194. Full article
(This article belongs to the Special Issue Machine Learning in Recommender Systems and Prediction Model)
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9 pages, 383 KiB  
Communication
Barycentric Kernel for Bayesian Optimization of Chemical Mixture
by San Kim and Jaekwang Kim
Electronics 2023, 12(9), 2076; https://doi.org/10.3390/electronics12092076 - 30 Apr 2023
Viewed by 1229
Abstract
Chemical-reaction optimization not only increases the yield of chemical processes but also reduces impurities and improves the performance of the resulting products, contributing to important innovations in various industries. This paper presents a novel barycentric kernel for chemical-reaction optimization using Bayesian optimization (BO), [...] Read more.
Chemical-reaction optimization not only increases the yield of chemical processes but also reduces impurities and improves the performance of the resulting products, contributing to important innovations in various industries. This paper presents a novel barycentric kernel for chemical-reaction optimization using Bayesian optimization (BO), a powerful machine-learning method designed to optimize costly black-box functions. The barycentric kernel is specifically tailored as a positive definite kernel for Gaussian-process surrogate models in BO, ensuring stability in logarithmic and differential operations while effectively mapping concentration space for solving optimization problems. We conducted comprehensive experiments comparing the proposed barycentric kernel with other widely used kernels, such as the radial basis function (RBF) kernel, across six benchmark functions in concentration space and three Hartmann functions in Euclidean space. The results demonstrated the barycentric kernel’s stable convergence and superior performance in these optimization scenarios. Furthermore, the paper highlights the importance of accurately parameterizing chemical concentrations to prevent BO from searching for infeasible solutions. Initially designed for chemical reactions, the versatile barycentric kernel shows promising potential for a wide range of optimization problems, including those requiring a meaningful distance metric between mixtures. Full article
(This article belongs to the Special Issue Machine Learning in Recommender Systems and Prediction Model)
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