Article
Computer Science, Information Systems
Ning Liu, Jianhua Zhao
Summary: In this paper, a recommendation system based on sentiment analysis and matrix factorization (SAMF) is proposed to solve the problems of data sparsity and credibility in collaborative filtering. By utilizing topic model and deep learning technology, the implicit information in reviews is fully mined to improve the rating matrix and assist in recommendation.
Article
Chemistry, Medicinal
Iori Azuma, Tadahaya Mizuno, Hiroyuki Kusuhara
Summary: This study proposed a new method called neighborhood regularized bidirectional matrix factorization (NRBdMF) to predict drug effects by incorporating bidirectionality. The NRBdMF model achieved high accuracy and interpretability in predicting both side effects and therapeutic effects.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Engineering, Multidisciplinary
Xujian Fang, Jiayi Wang, Dewen Seng, Binquan Li, Chenxuan Lai, Xiyuan Chen
Summary: This paper proposes the Local-Global Awareness Attention Model (LGAA) to model comment information, which calculates the importance of comments through local and global attention mechanisms, ultimately improving the performance of recommendation systems.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Engineering, Civil
Haicheng Qu, Jiangtao Guo, Yanji Jiang
Summary: In this study, we propose a recommendation algorithm called LG-DropEdge to mitigate overfitting issues in deep neural network-based recommendation algorithms. By combining lightweight graph convolutional networks and DropEdge techniques, the proposed algorithm can improve the precision of recommendation results by aggregating embedding results at different layers. Experimental results on multiple datasets demonstrate its effectiveness.
JOURNAL OF ADVANCED TRANSPORTATION
(2022)
Article
Computer Science, Artificial Intelligence
Guoshuai Zhao, Zhidan Liu, Yulu Chao, Xueming Qian
Summary: In this paper, a Context-Aware Personalized Emoji Recommendation (CAPER) model is proposed, which fuses contextual and personal information to improve recommendation accuracy. Experimental results show better performance of the CAPER model compared to existing methods, demonstrating the effectiveness of considering contextual and personal factors.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Quan Do, Wei Liu, Jin Fan, Dacheng Tao
Summary: This research proposes a method to discover explicit and implicit similarities across domains through matrix tri-factorization, improving the accuracy of cross-domain recommendations by preserving both shared and domain-specific factors. By utilizing explicit and implicit similarities, the approach outperforms existing algorithms by more than two times in recommendation accuracy.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Yuan-Yuan Xu, Shen-Ming Gu, Fan Min
Summary: This paper proposes an efficient and effective outlier removal algorithm to improve the quality of training data. By modeling the noise as a mixture of Gaussian distribution and calculating low-rank matrices, the algorithm compares the original and recovered ratings to identify suspected outliers.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Shulin Cheng, Huimin Jiang, Wanyan Wang, Wei Jiang
Summary: Compared to traditional recommender systems, context-aware recommender systems are better suited to real-world application contexts. However, most existing research has focused on single context-aware recommendations, such as time or location, and lacks in-depth analysis of multi-context-aware recommendations. Therefore, we proposed a high-order tensor factorization recommendation method based on multi-context awareness. By detecting user sensitivity to multiple contexts and constructing four-dimensional tensors and feature matrices, we were able to effectively address data sparsity. Our method improved recommendation quality through parameter optimization and filling in missing data, and was validated using a multi-context-aware movie dataset.
MULTIMEDIA SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Junrui Liu, Zhen Yang, Tong Li, Di Wu, Ruiyi Wang
Summary: This paper proposes a novel personalized recommendation method called similarity pairwise ranking (SPR) to address the issue of imbalanced data distribution affecting the effectiveness of Bayesian personalized ranking (BPR). By eliminating the score differences between popular and personalized items based on their similarity, SPR enhances the recommendation quality and better meets the individual needs of users. Experimental results demonstrate the superiority of SPR over recent state-of-the-art methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematics
Yongheng Mu, Yun Wu
Summary: Recommendation systems are widely used to provide personalized content and services to users efficiently. In this paper, a personalized multimodal movie recommendation system based on deep learning and multimodal data analysis was proposed. Real-world MovieLens datasets were used to test the effectiveness of the algorithm, which achieved improved accuracy in predicting movie scores compared to traditional collaborative filtering approaches. The combination of deep learning and multimodal data analysis can help alleviate the sparse data problem and enhance the performance of recommendation systems.
Article
Computer Science, Artificial Intelligence
Yuan-Yuan Xu, Shen-Ming Gu, Hua-Xiong Li, Fan Min
Summary: This paper introduces the problem of three-way conversational recommendation and proposes a hybrid conversational recommendation (HTCR) algorithm to address it. The algorithm takes into account human-machine interaction, misclassification, and promotion costs and achieves efficient recommendation through popularity and incremental matrix factorization techniques.
Article
Computer Science, Information Systems
Naina Yadav, Sukomal Pal, Anil Kumar Singh, Kartikey Singh
Summary: This paper introduces the collaborative filtering method for recommender systems, but points out the issues in diversity and coverage. To address this problem, the authors propose a cluster-based diversity recommendation method, which utilizes different clustering techniques and pre-trained models for generating diverse recommendations.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Weina Zhang, Xingming Zhang, Dongpei Chen
Summary: Implicit feedback data has various forms of interaction, such as clicking, collection, and play count, posing a challenge to recommendation systems. This paper introduces a Causal Neural Fuzzy Inference algorithm to address missing data in implicit recommendations through joint learning, demonstrating effectiveness and advancement in experiments on realistic datasets.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Heyong Wang, Zhenqin Hong, Ming Hong
Summary: This paper proposes an improved recommendation model called MFFR, which incorporates user reviews and ratings to enhance the accuracy of recommendations, particularly in cases of sparse ratings.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Xiaofeng Yuan, Lixin Han, Subin Qian, Licai Zhu, Jun Zhu, Hong Yan
Summary: The study introduces an imputation-based matrix factorization method to enhance the performance of collaborative filtering recommendation systems in addressing data sparsity issues, achieving superior recommendation accuracy on various datasets.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Alessandro Fogli, Giuseppe Sansonetti
PERSONAL AND UBIQUITOUS COMPUTING
(2019)
Article
Computer Science, Cybernetics
Giuseppe Sansonetti, Fabio Gasparetti, Alessandro Micarelli, Federica Cena, Cristina Gena
USER MODELING AND USER-ADAPTED INTERACTION
(2019)
Article
Computer Science, Information Systems
Giuseppe Sansonetti
PERSONAL AND UBIQUITOUS COMPUTING
(2019)
Article
Computer Science, Information Systems
Giuseppe D'Aniello, Matteo Gaeta, Francesco Orciuoli, Giuseppe Sansonetti, Francesca Sorgente
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)
Review
Materials Science, Multidisciplinary
Eli Saul Puchi-Cabrera, Edoardo Rossi, Giuseppe Sansonetti, Marco Sebastiani, Edoardo Bemporad
Summary: This article summarizes the recent application of machine learning tools in instrumented indentation testing and their significance in materials science. The combination of advanced nanomechanical/microstructural characterization, finite element simulation, and different machine learning algorithms can extract complex microstructure-property correlations and enhance the understanding of the microstructure-mechanical properties-performance relationships of materials.
CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE
(2023)
Article
Chemistry, Multidisciplinary
Alessio Ferrato, Carla Limongelli, Mauro Mezzini, Giuseppe Sansonetti
Summary: Nowadays, technology allows us to admire art remotely, but visiting art sites in person remains a unique experience. Monitoring and analyzing visitor behavior can enhance the on-site experience. This article proposes a novel approach to indoor tracking using low-cost equipment and deep neural networks, and it is evaluated in a real scenario.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Interdisciplinary Applications
Lorenzo Vaccaro, Giuseppe Sansonetti, Alessandro Micarelli
Summary: In this paper, the authors introduce the importance of AutoML in the field of computer science, discuss the pros and cons of some ML models and methods, and propose possible AutoML solutions. They analyze these solutions from theoretical and empirical perspectives, aiming to seek more effective AutoML frameworks.
Proceedings Paper
Computer Science, Interdisciplinary Applications
Lorenzo Vaccaro, Giuseppe Sansonetti, Alessandro Micarelli
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2020, PART IV
(2020)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Dalia Valeriani, Giuseppe Sansonetti, Alessandro Micarelli
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2020, PART IV
(2020)
Article
Computer Science, Information Systems
Giuseppe Sansonetti, Fabio Gasparetti, Giuseppe D'aniello, Alessandro Micarelli
Proceedings Paper
Computer Science, Theory & Methods
Giuseppe Sansonetti, Fabio Gasparetti, Alessandro Micarelli
ADJUNCT PUBLICATION OF THE 27TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (ACM UMAP '19 ADJUNCT)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Hebatallah A. Mohamed Hassan, Giuseppe Sansonetti, Fabio Gasparetti, Alessandro Micarelli
12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Domenico Giammarino, Davide Feltoni Gurini, Alessandro Micarelli, Giuseppe Sansonetti
ADJUNCT PUBLICATION OF THE 25TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'17)
(2017)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)