Review
Computer Science, Information Systems
Rui Chen, Kangning Pang, Min Huang, Hui Liang, Shizheng Zhang, Lei Zhang, Pu Li, Zhengwei Xia, Jianwei Zhang, Xiangjie Kong
Summary: With the development of online social networks, more and more users are participating and forming rich social relationships. These relationships provide a data source and research basis for recommender systems, driving the development of recommender systems based on social networks.
Article
Computer Science, Artificial Intelligence
Yangkun Li, Weizhi Ma, Chong Chen, Min Zhang, Yiqun Liu, Shaoping Ma, Yuekui Yang
Summary: Overfitting is a common problem in machine learning where the model fits the training data too closely but performs poorly on the test data. Dropout is an effective method for addressing overfitting, achieved by randomly dropping neurons or neural structures. However, the effectiveness, application scenarios, and contributions of various dropout methods have not been comprehensively summarized and compared. In this paper, we provide a systematic review of previous dropout methods, classify them into three categories based on the dropout operation stage, and discuss their application scenarios, connections, and contributions. Extensive experiments are conducted to verify the effectiveness of distinct dropout methods, and open problems and potential research directions are proposed for further exploration.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Yi Zuo, Shengzong Liu, Yun Zhou, Huanhua Liu
Summary: A social tagging system improves recommendation performance by utilizing tags as auxiliary information, which are text descriptions provided by individual users. However, there are challenges such as data sparsity, ambiguity, and difficulty in capturing multi-aspect user interests and item characteristics from these tags. To address these issues, a tag-aware recommendation model based on attention learning is proposed to capture diverse potential features for users and items.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Zhiliang Huang, Hong Ma, Shizhi Wang, Yuan Shen
Summary: This paper proposes a recommendation algorithm that integrates tag information and context information into the link relationship between users and items, achieving better recommendation accuracy compared to traditional recommendation algorithms.
COMPUTER COMMUNICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Sahraoui Dhelim, Nyothiri Aung, Mohammed Amine Bouras, Huansheng Ning, Erik Cambria
Summary: This survey examines the development and classification of personality-aware recommendation systems, discussing their design choices, personality modeling methods, recommendation techniques, and challenges.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Information Systems
Letian Wang, Yang Li, Weipeng Jing
Summary: Tag recommendation, a critical task in social media platforms, is challenging due to the constant influx of new content and terms as well as the creation of new tags. To address these challenges, KEIC, a tag recommendation framework that combines Knowledge Enhancement and Interclass Correlation, is proposed. KEIC enriches the semantic understanding of text by incorporating commonsense knowledge and identifies interclass correlations, effectively mitigating the long-tail effects of tags. Experiments on large-scale datasets demonstrate the seamless integration and outstanding performance of KEIC with existing classification-based tag recommendation models, without excessive parameter augmentation.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yonghong Yu, Xuewen Chen, Li Zhang, Rong Gao, Haiyan Gao
Summary: This article proposes a graph neural networks boosted personalized tag recommendation model to better learn the preferences and attribute features of entities. Additionally, a lightweight graph neural networks boosted personalized tag recommendation model is also proposed, and experimental results demonstrate that these models are more effective compared to traditional methods.
IEEE INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
Summary: Influenced by the success of deep learning, research in recommendation has shifted to developing new recommender models based on neural networks. This survey paper systematically reviews neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize the field and facilitate researchers and practitioners. It categorizes the work into collaborative filtering, content enriched recommendation, and temporal/sequential recommendation based on the data usage, and discusses promising directions in the field.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Karima Mecheri, Sihem Klai, Labiba Souici-Meslati
Summary: The objective of this paper is to study the state-of-the-art work on Web service recommender systems based on Deep Learning techniques and analyze their advantages and solutions. This will help readers understand this field and guide our future research directions in Web service recommendation.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Marwa Sharaf, Ezz El-Din Hemdan, Ayman El-Sayed, Nirmeen A. El-Bahnasawy
Summary: The recommendation system plays a crucial role in guiding users to find interesting content from a vast amount of information. This paper provides an overview of the recommendation system, its types, and its applications, with a specific focus on the finance recommendation system.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Review
Computer Science, Information Systems
Jing Qin
Summary: Recommender systems, a critical field of AI technology applications, have recently focused on long-tail item recommendation research to improve coverage and diversity, despite limited research literature available. This approach not only enhances the recommendation results but also prevents user fatigue.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2021)
Article
Computer Science, Information Systems
Hyeyoung Ko, Suyeon Lee, Yoonseo Park, Anna Choi
Summary: This paper reviews the research trends that connect advanced technical aspects of recommendation systems with their business applications. By analyzing a large number of articles, conference papers, and industry data, the study found a close relationship between the growth of recommendation system research and the business growth of applied service fields. This research provides a comprehensive summary and insights for researchers interested in recommendation systems.
Article
Computer Science, Software Engineering
Mansoureh Yari Eili, Jalal Rezaeenour
Summary: Recommender systems are widely used in various domains and can also be applied in business process execution to improve performance. However, more investigations are needed to deploy recommendation frameworks in process mining and ensure practical application of research findings.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Bo Chen, Yue Ding, Xin Xin, Yunzhe Li, Yule Wang, Dong Wang
Summary: AIRec is an attentive intersection model for TRS that constructs user representations through multi-layer perceptron and hierarchical attention network, enhances learning by exploiting intersection between user and item tags, and significantly improves tag-aware top-n recommendation.
Article
Computer Science, Information Systems
Weibin Zhao, Lin Shang, Yonghong Yu, Li Zhang, Can Wang, Jiajun Chen
Summary: Personalized tag recommender systems automatically recommend tags to users based on their past tagging information. In this research, a novel personalized tag recommendation model called DAE-PTR is proposed, which leverages the denoising auto-encoder framework to learn entity representations and capture complex relationships, leading to improved recommendation performance.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Philipe F. Melo, Daniel H. Dalip, Manoel M. Junior, Marcos A. Goncalves, Fabricio Benevenuto
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
(2019)
Article
Computer Science, Information Systems
Daniel Xavier Sousa, Sergio Canuto, Marcos Andre Goncalves, Thierson Couto Rosa, Wellington Santos Martins
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2019)
Article
Computer Science, Information Systems
Amir Khatibi, Fabiano Belem, Ana Paula Couto da Silva, Jussara M. Almeida, Marcos A. Goncalves
INFORMATION PROCESSING & MANAGEMENT
(2020)
Article
Computer Science, Information Systems
Washington Cunha, Sergio Canuto, Felipe Viegas, Thiago Salles, Christian Gomes, Vitor Mangaravite, Elaine Resende, Thierson Rosa, Marcos Andre Goncalves, Leonardo Rocha
INFORMATION PROCESSING & MANAGEMENT
(2020)
Article
Statistics & Probability
Thiago Salles, Leonardo Rocha, Marcos Goncalves
Summary: The study analyzed variants of random forest (RF) classifiers in the case of noisy data, exploring the bias-variance decomposition of error rate and showing significant improvements in variance and bias stability for lazy and boosted RF variants. The research provides promising directions for further enhancements in RF-based learners.
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
(2021)
Article
Computer Science, Information Systems
Fabiano M. Belem, Rodrigo M. Silva, Claudio M. de Andrade, Gabriel Person, Felipe Mingote, Raphael Ballet, Helton Alponti, Henrique P. de Oliveira, Jussara M. Almeida, Marcos A. Goncalves
INFORMATION PROCESSING & MANAGEMENT
(2020)
Article
Computer Science, Information Systems
Felipe Viegas, Mario S. Alvim, Sergio Canuto, Thierson Rosa, Marcos Andre Goncalves, Leonardo Rocha
INFORMATION SYSTEMS
(2020)
Article
Computer Science, Information Systems
Washington Cunha, Vitor Mangaravite, Christian Gomes, Sergio Canuto, Elaine Resende, Cecilia Nascimento, Felipe Viegas, Celso Franca, Wellington Santos Martins, Jussara M. Almeida, Thierson Rosa, Leonardo Rocha, Marcos Andre Goncalves
Summary: This article brings two major contributions. Firstly, it critically analyses recent scientific articles about different approaches for automatic text classification, revealing potential issues related to experimental procedures. Secondly, it provides a comparison between neural and non-neural ATC solutions, showing that simpler non-neural methods perform well in smaller datasets, while neural Transformers are better in larger datasets. However, the gains in effectiveness of neural methods are not significant compared to properly tuned non-neural solutions.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Review
Health Care Sciences & Services
Israel Junior Borges do Nascimento, Milena Soriano Marcolino, Hebatullah Mohamed Abdulazeem, Ishanka Weerasekara, Natasha Azzopardi-Muscat, Marcos Andre Goncalves, David Novillo-Ortiz
Summary: The study aimed to assess the impact of big data analytics on people's health, focusing on improving the accuracy of diagnosis for certain diseases, managing chronic diseases, and supporting real-time analysis of large, varied data inputs for disease prediction and diagnosis.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2021)
Article
Computer Science, Information Systems
Reinaldo Silva Fortes, Daniel Xavier de Sousa, Dayanne G. Coelho, Anisio M. Lacerda, Marcos A. Goncalves
Summary: The study introduces a new preference-based multi-objective recommendation method, IndED, which better satisfies individual user preferences and balances objectives more effectively. By utilizing the concepts of extreme dominance and statistical significance tests, IndED defines a new Pareto-based dominance relation to guide optimization search based on user preferences.
INFORMATION SCIENCES
(2021)
Article
Multidisciplinary Sciences
Amir Khatibi, Ana Paula Couto da Silva, Jussara M. Almeida, Marcos A. Goncalves
Article
Information Science & Library Science
Gustavo Oliveira de Siqueira, Sergio Canuto, Marcos Andre Goncalves, Alberto H. F. Laender
INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES
(2020)
Proceedings Paper
Computer Science, Information Systems
Pablo Figueira, Fabiano Belem, Jussara M. Almeida, Marcos A. Goncalves
2019 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES (JCDL 2019)
(2019)
Proceedings Paper
Computer Science, Information Systems
Sergio Canuto, Thiago Salles, Thierson C. Rosa, Marcos A. Goncalves
PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Felipe Viegas, Sergio Canuto, Christian Gomes, Washington Luiz, Thierson Rosa, Sabir Ribas, Leonardo Rocha, Marcos Andre Goncalves
PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19)
(2019)