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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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Proposing a Genetic Algorithms-Based data Selection Method for Imbalanced Medical datasets 15th

Proposing a Genetic Algorithms-Based Data Selection Method ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Salmi, Mabrouka Atif, Dalia Abraham, Ajith Ventura, Sebastian Laboratory of Applied Statistics (LASAP) National Higher School of Statistics and Applied Economics Tipaza Algeria Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Cordoba Cordoba Spain University Center Tipaza Algeria School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810794

The class imbalance issue is evident in medical datasets, posing a hurdle for accurate predictive modeling. Maintaining the natural characteristics of medical data while mitigating this issue is paramount in ensuring the success of clinical decision support systems. Therefore, this study proposes a genetic algorithm-based data selection method (GA-DS) for imbalanced medical data reserving its distribution and characteristics. It optimizes the assignment of samples into train and test sets by improving the recognition of rare samples on test data. We presented three versions of the GA-DS, one unconstrained and two constrained (train-set >=50%, train-set = 70%) to ensure the reliability of the results. Evaluation of the GA-DS methods on three commonly used imbalanced medical datasets and comparison with SMOTE, random oversampling, and stratified random sampling demonstrated the superior performance of GA-DS by improving the sensitivity score on test data. Future work will investigate its performance on large and highly imbalanced data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Class imbalance data distribution data selection genetic algorithms

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Fuzzychain: An equitable consensus mechanism for blockchain networks

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Journal of Network and Computer Applications 2025年 241卷

作者： Ramos-Cruz, Bruno Andreu-Perez, Javier Quesada-Real, Francisco J. Martínez, Luis Computer Science Department University of Jaen Jaen Jaen23071 Spain Centre for Computational Intelligencer School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence Jaen Jaen23071 Spain

Blockchain technology has become a trusted method for establishing secure and transparent transactions through a distributed, encrypted network. The operation of blockchain is governed by consensus algorithms, among which Proof of Stake (PoS) is popular yet has its drawbacks, notably the potential for centralising power in nodes with larger stakes or higher rewards. Our proposed novel solution, Fuzzychain, leverages fuzzy sets to define stake semantics, introducing a degree of softness in validator selection. This approach mitigates rigid threshold-based decision-making by allowing gradual transitions between stake levels, reducing sharp disparities among validators. By incorporating this enhanced stake evaluation, Fuzzychain promotes a more adaptive and distributed selection process, ensuring a fairer and more inclusive blockchain network. A thorough assessment of a real-time multi-agent blockchain system to examine validator selection and reduce inequality, promoting a more equitable distribution of stakes among validators compared to other consensus mechanisms. This fosters a more inclusive selection process and a more equitably distributed network. © 2025 The Authors

关键词： Consensus algorithm

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Transfer Learning with Foundational Models for Time Series Forecasting using Low-Rank Adaptations

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Information Fusion 2025年 123卷

作者： Germán-Morales, M. Rivera-Rivas, A.J. del Jesus Díaz, M.J. Carmona, C.J. Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science University of Jaen Jaen E-23071 Spain Leicester School of Pharmacy DeMontfort University Leicester LE1 7RH United Kingdom

Foundational Models are an emerging widely used technique of Generative Artificial intelligence. These models are distinguished by their scalability and the ease with which they can be adapted through the exploitation of Transfer Learning. The availability of high computational power and large datasets have supported their development, achieving a high generalization capacity due to the enormous and heterogeneous amounts of data used in their initial training. These characteristics contribute to a solid base that can be adapted or adjusted to a wide range of tasks, increasing their applicability. This study proposes the methodology LLIAM, a straightforward adaptation of a kind of Foundational Models, Large Language Models, for the Time Series Forecasting task. An adequate time-series prompting schema and Low-Rank Adaptations are used to enhance the knowledge of the model with diverse time series datasets, known as the fine-tuning phase. A study divided in two stages has been performed for evaluating the effectiveness of the proposed methodology. Initially, a comparison was made between the performance of LLIAM and different state-of-the-art Deep Learning algorithms, including Recurrent Neural Networks and Temporal Convolutional Networks, as well as a LLM-based method, TimeLLM. Following this, a zero-shot study is presented in order to evaluate the generalization capacity of the proposed methodology with time series datasets from unknown domains not considered in the model training. The outcomes of this investigation demonstrate the efficacy of LLIAM, highlighting that this straightforward and general approach can attain competent results without the necessity for applying complex modifications. This work also encourages the use of available resources (such as these pre-trained models) and efficient fine-tuning techniques to avoid unnecessary and costly training, narrowing the gap between the goals of traditional Artificial intelligence and Green Artificial Intellige

关键词： Transfer learning

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GPBus: Genetic Programming Based Automated Machine Learning for Bus Delay Prediction 28th

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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LSTM new gate for computing the efficiency on inputdata

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Knowledge-Based Systems 2025年 322卷

作者： García Cabello, Julia Carbó-García, Santiago Department of Applied Mathematics Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain

The recognized learning ability of neural networks (NNs) is determined by their training process. The NN data-dependent nature makes that their success depends to a large extent on the quality of the training data sets. This paper addresses the selection of the best training sets for Long Short Time Models (LSTMs) in real-life contexts. If such selection from a theoretical standpoint is already complex, it becomes challenging when addressed for real scenarios, where effectiveness should be replaced by efficiency (i.e., effectiveness in the shortest possible time and without increasing the computational cost). Our proposal revolves around the introduction, definition and structuring of a new LSTM gate: the efficiency gate, that applies to those inputs that have passed the forget gate. The efficiency gate is responsible for ensuring that training advances only with the best inputs in the sense that the corresponding outputs match as closely as possible with a target value (best estimators). For this purpose, new mathematical results will be demonstrated here in order to prove that our efficiency gate is mathematically feasible. We also fully describe how the new gate joins the LSTM operation, blending in with it. The consequence of such natural integration is that the computational effort is practically unchanged. Our methodology also allows the user to set the admissible error thresholds (which are different in each real scenario) as part of the setting options. © 2025 The Authors

关键词： Efficiency gate New LSTM gate Real-life contexts Selection of training data sets

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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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