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Past, present, and future of social commerce: a bibliometric analysis

Quality and Quantity 2025年 1-33页

作者： Herzallah, Doaa Liébana-Cabanillas, Francisco Muñoz-Leiva, Francisco Zarco, Carmen Department of Marketing and Market Research University of Granada Granada Spain Andalusian Institute of Research and Innovation in Tourism (IATUR) Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence (DASCI) Granada Spain

Given the significance of social commerce, extensive research has been conducted from various perspectives, resulting in numerous reviews and meta-analytical papers. However, these previous studies have highlighted information gaps and variations in approaches. Therefore, there is a need for a comprehensive examination of social commerce research, encompassing past, present, and future themes, as well as an investigation into additional features of social commerce. To address this need, the present study undertakes a comprehensive thematic review of social commerce research for two distinct periods: 2008–2018 and 2019–2022. Bibliometric analyses were conducted using data extracted from the Web of science database. The research employed science mapping techniques through the utilization of SciMAT and VOSviewer software. The findings of this study identified several themes related to social commerce during the first period, including product, perceived sociability, impulse buying, information technology, motivation, mobile commerce, and others, which were extensively explored. In the second period, themes such as UTAUT, SMEs, perceived risk, live stream, SOR (stimulus-organism-response), and the concept swift guanxi received significant attention. These findings, among others, contribute to the evolving understanding of the social commerce concept. By conducting a comprehensive thematic review and employing bibliometric analyses, this research fills existing knowledge gaps and provides a broader understanding of social commerce research across different periods. The findings shed light on the evolution of social commerce and pave the way for future investigations in this field. © The Author(s) 2025.

关键词： Bibliometric study Co-word analysis Social commerce

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An Analysis of Different Information Granularity Distribution Protocols to Improve Consistency in Intuitionistic Reciprocal Preference Relations 8th

An Analysis of Different Information Granularity Distributi...

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8th International Conference on Applied Cognitive Computing, ACC 2024, and 26th International Conference on Artificial intelligence, ICAI 2024, held as part of the World Congress in Computer science, Computer Engineering and Applied Computing, CSCE 2024

作者： González-Quesada, Juan Carlos Herrera-Viedma, Enrique Alonso, Sergio Morente-Molinera, Juan Antonio Pérez, Ignacio Javier Cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

ISBN: (纸本)9783031856273

Recently, an optimization of information granularity distribution has been proposed as a way in which the consistency of an intuitionistic reciprocal preference relation can be increased. Specifically, a symmetric and uniform distribution of information granularity has been taken into account for intuitionistic reciprocal preference relations. However, there are other information granularity distribution protocols that could also be used. The objective of this paper is to analyze all of them and determine their effectiveness in improving the consistency of an intuitionistic reciprocal preference relation. The performance of the protocols is discussed by conducting some numerical studies that help provide insights into the effectiveness of the protocols to increase the consistency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information granules

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On forecast stability

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International Journal of Forecasting 2025年

作者： Godahewa, Rakshitha Bergmeir, Christoph Erkin Baz, Zeynep Zhu, Chengjun Song, Zhangdi García, Salvador Benavides, Dario Department of Data Science and Artificial Intelligence Monash University Australia Department of Computer Science and Artificial Intelligence University of Granada Spain Data Science and Computational Intelligence Andalusian Institute DaSCI Andalucía Spain Meta Inc. CA United States

Forecasts are typically produced in a business context on a regular basis to make downstream decisions. Here, forecasts should not only be as accurate as possible, but also should not change arbitrarily, and be stable in some sense. In this paper, we explore two types of forecast stability that we call vertical stability (for forecasts from different origins for the same target) and horizontal stability (for forecasts from the same origin for different targets). Existing works in the literature are only applicable to certain base models and can only stabilise forecasts vertically. We propose a simple linear-interpolation-based approach to stabilise the forecasts provided by any base model, both vertically and horizontally. Our method makes the trade-off between stability and accuracy explicit, producing forecasts at any point in the spectrum of this trade-off. We used N-BEATS, pooled regression, LightGBM, ETS, and ARIMA as base models in our evaluation across different error and stability measures on four publicly available datasets. On some datasets, the proposed framework achieved forecasts that were both more accurate and stable than the base forecasts. On the others, we achieved forecasts that were slightly less accurate but much more stable. © 2025 International institute of Forecasters

关键词： Forecast combination Forecast congruence Forecast volatility Horizontal stability Instability Inter-plan stability Intra-plan stability Linear interpolation Nervousness Rolling origin stability Vertical stability

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Optimizing Version Innovation Age for Monitoring Markovian Source in Energy-Harvesting Systems

Optimizing Version Innovation Age for Monitoring Markovian S...

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IEEE Conference on Wireless Communications and Networking

作者： Mehrdad Salimnejad Anthony Ephremides Marios Kountouris Nikolaos Pappas Department of Computer and Information Science Linköping University Sweden Electrical and Computer Engineering University of Maryland College Park MD USA Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We study the real-time remote tracking of a two-state Markov process powered by an energy harvesting source. The source dynamically decides whether to transmit over an unreliable channel based on the state of the system. This problem is formulated as a Markov decision process (MDP) to determine the optimal transmission policy that minimizes the average Version Innovation Age (VIA) as a key performance metric. We demonstrate that the optimal transmission policy is threshold-based, determined by the battery level, source state, and VIA value. We numerically validate the analytical structure of the optimal policy and compare its performance against two baseline policies under various system parameters, establishing the superior performance of our approach.

关键词： Wireless communication Technological innovation Wireless sensor networks Energy consumption Markov decision processes Receivers Real-time systems Batteries Energy harvesting Monitoring

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Predict+Optimize Problem in Renewable Energy Scheduling

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IEEE Access 2025年 13卷 60064-60087页

作者： Bergmeir, Christoph de Nijs, Frits Genov, Evgenii Sriramulu, Abishek Abolghasemi, Mahdi Bean, Richard Betts, John Bui, Quang Dinh, Nam Trong Einecke, Nils Esmaeilbeigi, Rasul Ferraro, Scott Galketiya, Priya Glasgow, Robert Godahewa, Rakshitha Kang, Yanfei Limmer, Steffen Magdalena, Luis Montero-Manso, Pablo Peralta, Daniel Kumar, Yogesh Pipada Sunil Rosales-Pérez, Alejandro Ruddick, Julian Stratigakos, Akylas Stuckey, Peter Tack, Guido Triguero, Isaac Yuan, Rui DaSCI Andalusian Institute in Data Science and Computational Intelligence Granada18071 Spain University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain Monash University Department of Data Science and Artificial Intelligence MelbourneVIC3800 Australia Vrije Universiteit Brussel EVERGi MOBI Brussels1050 Belgium Queensland University of Technology Faculty of Science School of Mathematical Sciences BrisbaneQLD4000 Australia The University of Queensland Centre for Energy Data Innovation School of Information Technology and Electrical Engineering BrisbaneQLD4072 Australia The University of Adelaide School of Electrical and Electronics Engineering AdelaideSA5005 Australia Honda Research Institute Europe GmbH Offenbach63073 Germany Deakin University School of Information Technology MelbourneVIC3125 Australia Monash University Building and Property Division MelbourneVIC3800 Australia Beihang University School of Economics and Management Beijing100191 China Universidad Politécnica de Madrid E.T.S. Ingenieros Informáticos Madrid28660 Spain The University of Sydney Disciple of Business Analytics SydneyNSW2050 Australia Centro de Investigación en Matemáticas Department of Computer Science Monterrey66629 Mexico Mines Paris Sophia Antipolis06904 France Laboratory School of Computer Science NottinghamNG8 1BB United Kingdom

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization. © 2013 IEEE.

关键词： Optimization Forecasting Renewable energy sources Benchmark testing Australia Scheduling Production Costs Artificial neural networks Stochastic processes

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GPBus: Genetic Programming Based Automated Machine Learning for Bus Delay Prediction

GPBus: Genetic Programming Based Automated Machine Learning ...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Fuentes-Almoguera, Ángel García-Martínez, Carlos Luque, Gabriel Idener Seville41300 Spain University of Córdoba Córdoba14071 Spain Andalusian Research Institute in Data Science and Computational Intelligence University of Córdoba Córdoba14071 Spain ITIS Software University of Málaga Málaga29071 Spain

ISBN: (纸本)9783031900648

Urban bus services are a vital component of modern cities’ mobility. However, challenges such as traffic congestion, unpredictable weather, and operational incidents often cause delays, negatively impacting service quality and passenger experience. This study introduces GPBus, a genetic programming-based automated machine learning technique designed to predict bus delays. GPBus automates the selection and composition of predictive models while optimizing their hyperparameters. Additionally, the approach integrates transfer learning to reduce computational costs by reusing pretrained models. The proposed model was evaluated using real-world data from the public bus system in Málaga, Spain, spanning several months of operation. Experimental results demonstrate that GPBus significantly outperforms traditional machine learning methods, including Random Forest, Multi-Layer Perceptron, and Support Vector Machines, in terms of prediction accuracy. These results underscore the potential of AutoML approaches to enhance public transport systems by providing accurate and reliable delay predictions, ultimately improving user satisfaction and promoting sustainable urban mobility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

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A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

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Pattern Mining with Evolutionary Algorithms 1st ed. 2016

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2016年

作者： Sebastián Ventura José María Luna

ISBN: (数字)9783319338583

ISBN: (纸本)9783319338576

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and i

关键词： data mining. Pattern recognition systems.

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Trend analysis of the proportional allocation of funding by gender in Spanish National Research Projects: A study by disciplines and staff positions

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Journal of Informetrics 2025年第3期19卷

作者： Thamyres T. Choji Jose A. Moral-Munoz Manuel J. Cobo Department of Computer Science and Engineering University of Cádiz Puerto Real 11519 Spain Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada 18071 Spain Department of Nursing and Physiotherapy Faculty of Nursing and Physiotherapy University of Cádiz Cádiz 11009 Spain Biomedical Research and Innovation Institute of Cádiz (INiBICA) University of Cádiz Cádiz 11002 Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada 18071 Spain Unit for Computational Humanities and Social Sciences (U-CHASS) University of Granada Granada 18071 Spain

Given the critical role of research funding in driving knowledge production and promoting gender parity in academia, this study aimed to analyze gender differences in funding allocation over time across different disciplines or areas of knowledge and research staff categories. We analyzed data from 20,843 Spanish grants awarded between 2015 and 2022, matching these records with data on tenured research staff. Considering that in scientific systems, women represent a lower workforce, we analyzed the proportion of grants awarded relative to their presence as associate and full professors. To quantify these differences, we employed proportional gender funding and the women/men ratio. This approach was used to analyze whether the amount of grants awarded was proportional to their presence as tenured staff members. Our findings reveal significant disparities in grant allocation and tenured staff, with women receiving approximately 33% of the grants and representing 43% and 38% of associate and full professors, respectively. This difference was higher in terms of areas of knowledge, with Engineering and Architecture having the lowest women/men ratio and Arts and Humanities having the highest ratio. This pattern was repeated among associate and full professors with pronounced differences in Engineering and Architecture . Despite this, the longitudinal analysis showed that the differences decreased over time, showing a positive trend for both staff categories and across different areas of knowledge. Regarding proportionality, we observed that the proportion of grants awarded to women agreed with their representation as tenured staff, reaching its highest value by 2022. In areas where women received fewer grants and were fewer associate and full professors, such as Engineering and Architecture , the proportion of grants awarded to women was similar to that of men. Although our findings indicate a positive trend favoring women, more action needs to be taken. Future research c

关键词： Gender-differences SDG-5 Grants Research-staff Trend-analysis

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A Web Group Decision Support System Based on Granular Computing 21

A Web Group Decision Support System Based on Granular Comput...

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Cabrerizo, Francisco Javier González-Quesada, Juan Carlos Pérez, Ignacio Javier Cobo, Manuel Jesús Herrera-Viedma, Enrique Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

ISBN: (纸本)9781643683164

Achieving a good agreement level between decision-makers is a crucial point to solve group decision-making processes. Habitually, it is the duty of the moderator, a person in charge of assuring that the consensus reaching process is conducted correctly. This person also offers advice to the decision-makers with the objective that they modify their assessments and narrow their differences. A great number of theoretical models have been introduced to help or substitute the moderator's tasks. However, few of them have been implemented in practice. In this study, we present a web group decision support system helping, and even substituting, the moderator's functions during the whole decision-making process. This system is based on the granular computing paradigm to improve both the consistency and the consensus achieved between the decision-makers. In addition, the system improves them with the possible minimum adjustment, i.e., it tries to modify the decision-makers' assessments as minimum as possible. This group decision support system provides a web interface allowing to conduct group decision-making processes in which the decision-makers do not have the choice to meet together physically. © 2022 The authors and IOS Press. All rights reserved.

关键词： Decision support systems

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