Article
Computer Science, Information Systems
Lujie Zhou, Yuxin Mao, Naixue Xiong, Yangfan Wang, Feng Feng
Summary: This study proposes an effective scheme for detecting business-related hot topics in professional social networks (PSNs) by constructing a heterogeneous network and extending the PageRank algorithm. The results show that the proposed method achieves higher coverage rate and distinction degree compared to existing methods, by considering affiliation relationships, users' contributions, and following relationships.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Jose A. Diaz-Garcia, M. Dolores Ruiz, Maria J. Martin-Bautista
Summary: This paper discusses the problem of social media mining and the application of unsupervised techniques, particularly association rules. It provides a broad overview of the applications of association rules in social media mining, focusing on their application to mining textual entities such as tweets. The strengths and weaknesses of using association rules for different tasks in textual social media are also discussed. Finally, the paper provides a perspective on the challenges that association rules will face in the next decade within the field of social media mining.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Theory & Methods
Pablo Sanchez, Alejandro Bellogin
Summary: In this paper, a systematic review is presented on the research conducted in the past 10 years about Point-of-Interest recommendation. The algorithms and evaluation methodologies used in these works are discussed and categorized, and the opportunities and challenges in the field are pointed out. The lack of reproducibility in the field is also examined, which may hinder real performance improvements.
ACM COMPUTING SURVEYS
(2022)
Article
Computer Science, Artificial Intelligence
Ivan Palomares, Carlos Porcel, Luiz Pizzato, Ido Guy, Enrique Herrera-Viedma
Summary: This paper discusses decision-making under information overload on the Internet and the role of recommender systems as personalized decision support tools. Specifically, it introduces the concept of Reciprocal Recommender System (RRS) where users act as both the recommenders and the recommendees.
INFORMATION FUSION
(2021)
Article
Computer Science, Artificial Intelligence
Yu Lei, Zhitao Wang, Wenjie Li, Hongbin Pei, Quanyu Dai
Summary: This paper proposes a method to address the issues of data sparsity and cold-start in recommender systems by leveraging social networks. Two algorithms based on this method are developed and the experimental results show their outstanding performance on real-world datasets with reasonable computation cost.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Mathematics, Applied
Yan-Li Lee, Tao Zhou, Kexin Yang, Yajun Du, Liming Pan
Summary: This paper proposes a recommendation algorithm that combines social relationships and historical behaviors, and tests its performance on real networks. The results show that the algorithm outperforms the benchmarks in terms of accuracy and diversity metrics, and has a significant improvement in the recommendation performance for cold-start users.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Review
Computer Science, Artificial Intelligence
Parisa Abolfath Beygi Dezfouli, Saeedeh Momtazi, Mehdi Dehghan
Summary: Users' reviews are valuable for recommendation systems and can help alleviate data sparsity issues. The MatchPyramid Recommender System (MPRS) presented in this paper leverages review texts to predict user ratings for items, treating recommendation as a text matching problem. Experimental results show relative improvements compared to existing models on different datasets, demonstrating the effectiveness of leveraging review texts in recommendations.
APPLIED SOFT COMPUTING
(2021)
Review
Computer Science, Artificial Intelligence
R. J. Kuo, Shu-Syun Li
Summary: With the rapid development of electronic commerce, the recommender system has emerged to assist users' decision-making processes and enable precision marketing. This study utilized the PSO algorithm to solve the problem of data sparsity, while BERT was applied to extract consumer feedback characteristics. The combination of different data types improved the recommendation performance, as evidenced by outperforming existing methods on Amazon datasets in terms of mean absolute error and mean squared error.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Jun Zhu, Lixin Han, Zhinan Gou, Yi Yang, Xiaofeng Yuan, Jingxian Li, Shu Li
Summary: This paper introduces an ensemble-based personalized location recommendation algorithm to ensure the robustness of recommendation systems in terms of accuracy and stability. Experimental results demonstrate the algorithm's superior performance in prediction accuracy and system stability.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Green & Sustainable Science & Technology
Shao-Chao Ma, Jin-Hua Xu, Ying Fan
Summary: This study examines patents related to electric vehicle (EV) technology from 1970 to 2016 to understand the key characteristics and trends of its rapid development. Using text mining, clustering, and social network analysis, the study identifies the main holders and development trajectory of EV technology. The research finds that safely and quickly charging batteries and distributing energy to storage units is a major concern, involving battery technology, charging facilities, and power control systems. Wireless charging technology is identified as the research frontier of EV technology.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Computer Science, Information Systems
Anitha Anandhan, Maizatul Akmar Ismail, Liyana Shuib, Wan Siti Nur Aiza, Monther M. Elaish
Summary: The increasing popularity of social media has led to the need for recommender systems that cater to multiple fields. This study uses bibliometric analysis to provide researchers with measures to compare and improve citation rates for new publications.
Review
Computer Science, Information Systems
Areej Bin Suhaim, Jawad Berri
Summary: Context-aware recommender systems for online social networks have seen significant growth in recent years, driven by the widespread use of smartphones and cutting-edge web technologies. This research provides a comprehensive review of these systems, focusing on approaches and techniques used, as well as identifying research gaps, challenges, and opportunities. Through an analysis of research articles from 2015 to 2020, this study presents detailed findings, proposes evaluation tools, and suggests future research directions in this field.
Article
Computer Science, Information Systems
Jurgen Schedlbauer, Georgios Raptis, Bernd Ludwig
Summary: This study used web crawling and text mining techniques to analyze German job advertisements, finding that soft skills and professional expertise are equally important. The results highlight the importance of practical experience and can guide the development of medical informatics curricula.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Fabio Gasparetti, Giuseppe Sansonetti, Alessandro Micarelli
Summary: This paper discusses the importance of community detection techniques in improving recommender systems based on information extracted from social network services. It aims to provide a narrative review of the main outcomes and research directions in this field, benefiting researchers and practitioners in recommender systems and social media.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Matthew Shardlow, Luciano Gerber, Raheel Nawaz
Summary: This work uncovers the polysemous nature of emoji and develops a corpus to predict their meanings, highlighting the importance of considering the meaning behind emoji.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Statistics & Probability
Argimiro Arratia, Alejandra Cabana, Enrique M. Cabana
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2018)
Article
Computer Science, Artificial Intelligence
Argimiro Arratia, Lluis A. Belanche, Luis Fabregues
NEURAL PROCESSING LETTERS
(2020)
Article
Business, Finance
Argimiro Arratia, Albert Dorador
QUANTITATIVE FINANCE
(2019)
Article
Economics
Argimiro Arratia, Henryk Gzyl
COMPUTATIONAL ECONOMICS
(2020)
Article
Multidisciplinary Sciences
Amanda Fernandez-Fontelo, David Morina, Alejandra Cabana, Argimiro Arratia, Pere Puig
Article
Public, Environmental & Occupational Health
David Morina, Amanda Fernandez-Fontelo, Alejandra Cabana, Argimiro Arratia, Gustavo Avalos, Pedro Puig
Summary: This study estimated the actual number of COVID-19 cases in Spain using a hierarchical Bayesian model, showing that the real case load was significantly higher than officially reported. The results were found to be quite accurate when compared with the results of a second wave of the Spanish seroprevalence study.
EUROPEAN JOURNAL OF PUBLIC HEALTH
(2021)
Article
Computer Science, Artificial Intelligence
Argimiro Arratia, Marti Renedo Mirambell
Summary: The study introduces a systematic approach for validating clustering results on weighted networks, assessing significance and stability through community scoring functions and a non-parametric bootstrap method. Testing on synthetic and real world networks identifies best performing algorithms, suggesting adequacy for cases with unknown clustering structures. The methods are implemented in R and will be released in the upcoming clustAnalytics package.
PEERJ COMPUTER SCIENCE
(2021)
Article
Mathematics
Argimiro Arratia, Henryk Gzyl, Silvia Mayoral
Summary: This work presents a method to improve the return of a well diversified portfolio while maintaining its diversification. By constructing a neighborhood of the portfolio and using the maximum entropy in the mean method, a portfolio that maximizes the return within that neighborhood is found. The implicit advantage of this method is that the resulting portfolio will also have acceptable risk and diversification if the benchmark portfolio meets those criteria.
Proceedings Paper
Computer Science, Artificial Intelligence
Argimiro Arratia
Summary: This paper offers practical suggestions for utilizing financial news-based sentiment indicators in trading, portfolio selection, assets' industry classification, and risk management.
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Argimiro Arratia, Alejandra Cabana, Jose Rafael Leon
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT I
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Argimiro Arratia, Eduardo Sepulveda
MINING DATA FOR FINANCIAL APPLICATIONS
(2020)
Article
Energy & Fuels
Albert Calvo, Bernat Coma-Puig, Josep Carmona, Marta Arias
Article
Business, Finance
Gero Junike, Argimiro Arratia, Alejandra Cabana, Wim Schoutens
EUROPEAN JOURNAL OF FINANCE
(2020)
Proceedings Paper
Computer Science, Information Systems
Jose Suarez-Varela, Sergi Carol-Bosch, Krzysztof Rusek, Paul Almasan, Marta Arias, Pere Barlet-Ros, Albert Cabellos-Aparicio
PROCEEDINGS OF THE 2019 ACM SIGCOMM CONFERENCE POSTERS AND DEMOS (SIGCOMM '19)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Marta Arias, Argimiro Arratia, Ariel Duarte-Lopez
RECENT ADVANCES IN ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT
(2017)
Article
Computer Science, Information Systems
Alessio Cecconi, Luca Barbaro, Claudio Di Ciccio, Arik Senderovich
Summary: This paper introduces a framework for designing probabilistic measures for declarative process specifications, which can assess the degree of compliance between process data and specifications. Through experiments, the applicability of the approach for various process mining tasks is demonstrated.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Mahei Manhai Li, Philipp Reinhard, Christoph Peters, Sarah Oeste-Reiss, Jan Marco Leimeister
Summary: This article introduces a novel human-in-the-loop (HIL) design for ITSM support ticket recommendations by incorporating a value co-creation perspective. The design incentivizes ITSM agents to provide labels during their everyday ticket-handling procedures, and the evaluation shows that recommendations after label improvement have increased user ratings.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Anton Yeshchenko, Jan Mendling
Summary: This paper presents the development of event sequence data analysis techniques in different fields and proposes an integrated framework to facilitate collaboration and research synergy across various domains.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Iris Reinhartz-Berger, Alan Hartman, Doron Kliger
Summary: Many IT departments provide solutions that partially meet the needs of business units. This research aims to develop a data-driven analysis method to support the selection of solutions with higher prospects of adoption and identify design gaps and barriers.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Orlenys Lopez-Pintado, Marlon Dumas, Jonas Berx
Summary: Business process simulation is a versatile technique that predicts the impact of changes on process performance. However, previous approaches have limitations due to their treatment of resources as undifferentiated entities. This article addresses this issue by proposing a new simulation approach that treats each resource as an individual entity with its own performance and availability. The article also presents methods for discovering simulation models with differentiated resources and optimizing resource availability calendars. Empirical evaluation demonstrates that differentiated resource models better replicate cycle time distributions and work rhythm, and iterative optimization of resource allocations and calendars leads to improved cost-time tradeoffs.
INFORMATION SYSTEMS
(2024)
