Article
Computer Science, Information Systems
Sara Latifi, Dietmar Jannach, Andres Ferraro
Summary: Sequential recommendation problems have attracted increasing research interest recently. Nearest-neighbor methods can achieve comparable or better performance than the latest Transformer-based techniques in certain cases, while deep learning methods outperform simpler ones with larger datasets, caution is advised when using sampled metrics.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jinfeng Fang, Xiangfu Meng
Summary: This paper proposes an approach for next POI recommendation based on user relationship and preference information. By learning user relationship vectors and combining short-term and long-term modules, the method obtains scores for POIs and achieves significant improvements in user recommendation, as demonstrated by extensive experiments.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematics
Lili Wang, Sunit Mistry, Abdulkadir Abdulahi Hasan, Abdiaziz Omar Hassan, Yousuf Islam, Frimpong Atta Junior Osei
Summary: The study presents an architecture for a recommendation system that transforms user items into narrow categories. It focuses on identifying movies that a user will likely watch based on their favorite items. The system prioritizes the shortest connections between item correlations and utilizes various methods to reduce data sparsity. It also demonstrates the ability to provide moderate recommendations from diverse perspectives.
Article
Chemistry, Multidisciplinary
Claudia N. Sanchez, Julieta Dominguez-Soberanes, Alejandra Arreola, Mario Graff
Summary: With the growth of food-delivery applications, creating new recommendation systems tailored to this platform is crucial. The document proposes a recommendation system based on the number of orders stored by a real food-delivery application, using the nearest-neighbor technique to calculate the client's preferred restaurants and analyze other clients with similar buying patterns. The system has been validated using a real dataset and outperformed other state-of-the-art recommendation techniques.
APPLIED SCIENCES-BASEL
(2023)
Article
Multidisciplinary Sciences
Lan Ngoc Nguyen, Thanh-Hai Le, Linh Quy Nguyen, Van Quan Tran
Summary: This study establishes a machine learning model to predict the CT Index of asphalt concrete and compares the performance of three different machine learning methods. The results show that the Random Forest model is the most effective. The study also identifies the important factors that affect the variation of the CT Index.
Article
Computer Science, Information Systems
Mahesh Thyluru Ramakrishna, Vinoth Kumar Venkatesan, Rajat Bhardwaj, Surbhi Bhatia, Mohammad Khalid Imam Rahmani, Saima Anwar Lashari, Aliaa M. Alabdali
Summary: The emergence of social media platforms has greatly improved social connections. However, finding the right friends remains a challenge. This study proposes a social and semantic-based collaborative filtering approach to enhance personalized recommendations. The results show that this approach improves recommendation accuracy and addresses the issues of sparsity and cold start.
Article
Automation & Control Systems
J. A. Romero-del-Castillo, Manuel Mendoza-Hurtado, Domingo Ortiz-Boyer, Nicolas Garcia-Pedrajas
Summary: Multi-label learning is an important field in machine learning research, and the multi-label k-nearest neighbor method is one of the most successful algorithms. However, allocating the appropriate value of k is a challenge in difficult classification tasks, as different regions may require different k values. We propose a simple yet powerful method to set local k values, obtaining the optimal value by optimizing the local effect of different k values near each prototype.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Dhawan Sanjeev, Kulvinder Singh, Eduard-Marius Craciun, Adrian Rabaea, Amit Batra
Summary: In modern times, next-cart recommendation (NCR) has become a dominant topic in e-commerce research, utilizing recurrent neural networks (RNNs) for recommendation and sequential modeling. However, existing methods fail to fully exploit the significant signals present in personalized product frequency (PPF) information.
NEURAL PROCESSING LETTERS
(2023)
Article
Statistics & Probability
Emre Demirkaya, Yingying Fan, Lan Gao, Jinchi Lv, Patrick Vossler, Jingbo Wang
Summary: This work introduces a novel two-scale DNN method by linearly combining two DNN estimators with different subsampling scales to reduce bias and achieve the optimal nonparametric convergence rate under the fourth-order smoothness condition.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2022)
Article
Computer Science, Artificial Intelligence
Yongda Cai, Joshua Zhexue Huang, Jianfei Yin
Summary: This paper proposes a new method called adaptive k-nearest neighbors similarity graph (AKNNG) for constructing a better graph structure. By assigning different k values to different data points and automatically adjusting the k value based on the similarity graph, the AKNNG method improves clustering accuracies and reduces construction time.
Article
Computer Science, Artificial Intelligence
Zhibin Pan, Yiwei Pan, Yidi Wang, Wei Wang
Summary: The LMKNN classifier has better performance and robustness compared to the KNN classifier, but the unreliable nearest neighbor selection rule and single local mean vector strategy severely impact its classification performance.
Article
Computer Science, Artificial Intelligence
Yasir Aziz, Kashif Hussain Memon
Summary: The K-Nearest Neighbor (KNN) algorithm is crucial in data science and machine learning. Recent research has focused on distance computations and finding the optimal value of K, which has made neighborhood extraction slow. This study proposes a fast geometrical approach for neighborhood extraction that eliminates the need for distance computations and instead creates a geometrical shape based on the number of data features.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Jianyu Zheng, Ying Liu
Summary: This study used probing methods to identify syntactic knowledge in the attention heads and hidden states of Chinese BERT. The results showed that certain individual heads and combinations of heads performed well in encoding specific and overall syntactic relations, respectively. The hidden representations in each layer also contained varying degrees of syntactic information. The analysis of fine-tuned models for different tasks revealed changes in language structure conservation. These findings help explain the significant improvements achieved by Chinese BERT in various language-processing tasks.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Maximiliano Cubillos, Sanne Wohlk, Jesper N. Wulff
Summary: This study proposes a bi-objective algorithm based on the k-nearest neighbors method for imputing missing values in data with continuous variables and multilevel structures. Results from simulation studies show that the proposed method outperforms benchmark methods in cases with high intraclass correlation, reducing estimation bias and coefficient of determination.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Alex X. Wang, Stefanka S. Chukova, Binh P. Nguyen
Summary: k-nearest neighbors (k-NN) is a well-known classification algorithm that is widely used in different domains. We proposed the Centroid Displacement-based k-NN algorithm to address the issue of class determination in standard k-NN algorithm. Our experimental results demonstrate that our algorithm is able to enhance the classification performance of the standard k-NN algorithm and its variants and also improve the computational efficiency.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Icaro Cavalcante Dourado, Renata Galante, Marcos Andre Goncalves, Ricardo da Silva Torres
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
(2019)
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, 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)
Article
Computer Science, Information Systems
Sang-Bing Tsai, Xusen Cheng, Yanwu Yang, Jason Xiong, Alex Zarifis
Summary: This article structurally concludes the methods proposed and evidenced to develop digital entrepreneurship from a socio-technical perspective. The technology itself and the process of utilization should be carefully considered. From a social perspective, fulfilling the needs of customers in social interaction and nurturing characteristics and social skills for the digital work environment are crucial.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xiaochang Fang, Hongchen Wu, Jing Jing, Yihong Meng, Bing Yu, Hongzhu Yu, Huaxiang Zhang
Summary: This study proposes a novel fake news detection framework, utilizing news semantic environment perception (NSEP) to identify fake news content. The framework consists of steps such as dividing the semantic environment into macro and micro levels, applying graph convolutional networks, and utilizing multihead attention. Empirical experiments show that the NSEP framework achieves high accuracy in detecting Chinese fake news, outperforming other baseline methods and highlighting the importance of both micro and macro semantic environments in early detection of fake news.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xudong Sun, Alladoumbaye Ngueilbaye, Kaijing Luo, Yongda Cai, Dingming Wu, Joshua Zhexue Huang
Summary: This paper proposes a scalable distributed frequent itemset mining (ScaDistFIM) algorithm to address the data scalability and flexibility issues in basket analysis in the big data era. Experiment results demonstrate that the ScaDistFIM algorithm is more efficient compared to the Spark FP-Growth algorithm.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Boxu Guan, Xinhua Zhu, Shangbo Yuan
Summary: This paper aims to improve the interpretability of machine reading comprehension models by utilizing the pre-trained T5 model for evidence inference. They propose an interpretable reading comprehension model based on T5, which is trained on a more accurate evidence corpus and can infer precise interpretations for answers. Experimental results show that their model outperforms the baseline BERT model on the SQuAD1.1 task.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Yanhao Wang, Baohua Zhang, Weikang Liu, Jiahao Cai, Huaping Zhang
Summary: In this study, we propose a data augmentation-based semantic text matching model called STMAP. By using Gaussian noise and noise mask signal for data augmentation, as well as employing an adaptive optimization network for training target optimization, our model achieves good performance in few-shot learning and semantic deviation problems.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Jiahao Yang, Shuo Feng, Wenkai Zhang, Ming Zhang, Jun Zhou, Pengyuan Zhang
Summary: To pursue profit from stock markets, researchers utilize deep learning methods to forecast asset price movements. However, there are two issues in current research, the discrepancy between forecasting results and profits, and heavy reliance on prior knowledge. To address these issues, researchers propose a novel optimization objective and modeling method, and conduct experiments to validate their approach.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Heng Zhang, Chengzhi Zhang, Yuzhuo Wang
Summary: This study provides an accurate analysis of technology development in the field of Natural Language Processing (NLP) from an entity-centric perspective. The findings indicate an increase in the average number of entities per paper, with pre-trained language models becoming mainstream and the impact of Wikipedia dataset and BLEU metric continuing to rise. There has been a surge in popularity for new high-impact technologies in recent years, with researchers accepting them at an unprecedented speed.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Davide Buscaldi, Danilo Dessi, Enrico Motta, Marco Murgia, Francesco Osborne, Diego Reforgiato Recupero
Summary: In scientific papers, citing other articles is a common practice to support claims and provide evidence. This paper proposes two automatic methods using Transformer models to address citation placement, and achieves significant improvements in experiments.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Baozhuang Niu, Lingfeng Wang, Xinhu Yu, Beibei Feng
Summary: This paper examines whether the incumbent brand should adopt digital technology to forecast demand and adjust order decisions in the face of soaring demand for medical supply caused by frequent outbreaks of regional COVID-19 epidemic. The study finds that digital transformation can lead to a triple-win situation among the incumbent brand, social welfare, and consumer surplus, as well as bring benefits to the manufacturer. Furthermore, the research provides insights for firms' digital entrepreneurship decisions through theoretical optimization and data processing/policy simulation.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xueyang Qin, Lishang Li, Fei Hao, Meiling Ge, Guangyao Pang
Summary: Image-text retrieval is important in connecting vision and language. This paper proposes a method that utilizes prior knowledge to enhance feature representations and optimize network training for better retrieval results.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Review
Computer Science, Information Systems
Gang Ren, Lei Diao, Fanjia Guo, Taeho Hong
Summary: This paper proposes a novel approach for predicting the helpfulness of reviews by utilizing both textual and image features. The proposed method considers the correlation between features through self-attention and co-attention mechanisms, and fuses multi-modal features for prediction. Experimental results demonstrate the superior performance of the proposed method compared to benchmark methods.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhongquan Jian, Jiajian Li, Qingqiang Wu, Junfeng Yao
Summary: Aspect-Level Sentiment Classification (ALSC) is a crucial challenge in Natural Language Processing (NLP). Most existing methods fail to consider the correlations between different instances, leading to a lack of global viewpoint. To address this issue, we propose a Retrieval Contrastive Learning (RCL) framework that extracts intrinsic knowledge across instances for improved instance representation. Experimental results demonstrate that training ALSC models with RCL leads to substantial performance improvements.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Ying Hu, Yanping Chen, Ruizhang Huang, Yongbin Qin, Qinghua Zheng
Summary: Biomedical relation extraction aims to extract the interactive relations between biomedical entities in a sentence. This study proposes a hierarchical convolutional model to address the semantic overlapping and data imbalance problems. The model encodes both local contextual features and global semantic dependencies, enhancing the discriminability of the neural network for biomedical relation extraction.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhou Yang, Yucai Pang, Xuehong Li, Qian Li, Shihong Wei, Rong Wang, Yunpeng Xiao
Summary: This study proposes a rumor detection model based on topic audiolization, which transforms the topic space into audio-like signals. Experimental results show that the model achieves significant performance improvements in rumor identification.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Alistair Moffat
Summary: This paper proposes the buying power metric for assessing the quality of product rankings on e-commerce sites. It discusses the relationship between the buying power metric and user reactions, and introduces an alternative product ranking effectiveness metric.
INFORMATION PROCESSING & MANAGEMENT
(2024)