Article
Computer Science, Artificial Intelligence
Fenfang Xie, Angyu Zheng, Liang Chen, Zibin Zheng
Summary: This paper introduces an approach called AMERec based on Attentive Meta-graph Embedding for item Recommendation in HINs to address the issues mentioned above. The method prioritizes highly similar pairwise features, differentiates each node in the meta-graph, and learns an embedding for each meta-graph. It also considers the differences between user and item pairs based on their meta-graph context and predicts ratings by capturing interaction information between users, items, and their meta-graph based context.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Seungyeon Lee, Dohyun Kim
Summary: In this paper, a recommender system based on convolutional neural network is proposed to capture the complex interactions between users and items, giving greater weight to important features and alleviate the overfitting issue. Experiments show that the proposed method outperforms existing methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Suiyu Zhang, Xiaoyu Ma, Yaqi Wang, Yijie Zhou, Dingguo Yu
Summary: The study proposes a model that independently embeds ID features and effectively learns their interactions, improving the accuracy and convergence speed of deep recommendation models.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Zhibin Hu, Jiachun Wang, Yan Yan, Peilin Zhao, Jian Chen, Jin Huang
Summary: The neural graph personalized ranking (NGPR) model proposed in this study directly incorporates the user-item interaction graph in embedding learning to address the lack of correlation between users and items in existing methods. Extensive experiments demonstrate the superior performance of NGPR on personalized ranking tasks.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Pratik K. Biswas, Songlin Liu
Summary: In this paper, a hybrid recommender system that combines collaborative filtering with deep learning is proposed to enhance recommendation performance and overcome the limitations of collaborative filtering. By combining the outputs of collaborative filtering with a deep neural network in a big data processing framework, the proposed system outperforms existing hybrid recommender systems in recommending smartphones to prospective customers.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Taher Hekmatfar, Saman Haratizadeh, Sama Goliaei
Summary: In this paper, we propose PGRec, a novel model-based ranking-oriented recommendation framework that extracts vector representations from PrefGraph to predict user preferences and generate recommendation lists. Experimental results show that PGRec outperforms other model-based and neighborhood-based recommendation algorithms in terms of the NDCG metric.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Soo-Yeon Jeong, Young-Kuk Kim
Summary: This paper proposes a deep learning-based context-aware recommender system that effectively addresses data sparsity and demonstrates superior performance across various datasets.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Surong Yan, Haosen Wang, Yixiao Li, Yuan Zheng, Long Han
Summary: In this study, an attention-aware metapath-based network embedding method is proposed to address the issue of neglecting semantic differences between different metapaths in existing HIN based recommendation methods. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art recommendation methods, solves the data sparsity problem, and models the multiple semantic information of users and items effectively.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Biochemical Research Methods
Kanggeun Lee, Dongbin Cho, Jinho Jang, Kang Choi, Hyoung-oh Jeong, Jiwon Seo, Won-Ki Jeong, Semin Lee
Summary: In this study, we propose a novel multidrug response prediction framework, which utilizes a Bayesian neural network and soft-supervised contrastive regularization. The framework overcomes the prediction accuracy limitation induced by imbalanced data and achieves better performance than previous methods. It also predicts missing drug responses that were not included in public databases.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Kanggeun Lee, Dongbin Cho, Jinho Jang, Kang Choi, Hyoung-oh Jeong, Jiwon Seo, Won-Ki Jeong, Semin Lee
Summary: This study proposes a novel multidrug response prediction framework, RAMP, which overcomes the prediction accuracy limitation induced by the imbalance of trained response data and predicts many missing drug responses. RAMP proves to be suitable for high-throughput prediction of cancer drug sensitivity and useful for guiding drug selection processes.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Zafar Ali, Guilin Qi, Khan Muhammad, Siddhartha Bhattacharyya, Irfan Ullah, Waheed Abro
Summary: The large number of research articles on the Web poses challenges for researchers to find related works, leading to the development of network representation learning-based citation recommendation models. Our proposed model effectively utilizes semantic relations and contextual information within bibliographic networks, demonstrating significant improvements compared to baseline models on DBLP datasets.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yi-Hung Liu, Yen-Liang Chen, Po-Ya Chang
Summary: With the increasing number of mobile applications, it has become difficult for users to find the most suitable and interesting ones. This study proposes a better model for mobile app recommendation by combining matrix factorization, user reviews, and deep learning methods. Experimental results show that this model outperforms existing methods.
DECISION SUPPORT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zafar Ali, Guilin Qi, Khan Muhammad, Pavlos Kefalas, Shah Khusro
Summary: The study introduces a network embedding model called GCR-GAN for global citation recommendation, which shows promising results in generating personalized citation recommendations using HBN and learning semantic-preserving graph representations with SPECTER and Denoising Auto-encoder networks. Compared to baseline models, it achieves an improvement of nearly 11% and 12% in terms of MAP and nDCG metrics, respectively, and also performs well in addressing the network sparsity issue.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Tianjun Wei, Tommy W. S. Chow
Summary: This paper introduces a unified graph recommendation framework that combines graph convolution networks (GCN) and traditional models, and proposes a novel Fused Graph Context-aware Recommender system (FGCR) model to address limitations of existing models. FGCR models robust node relationships in the user-item-context interaction graph using a sparse item correlation matrix and dense node embeddings, and applies a novel masked graph convolution strategy for refining the information aggregation process. Experimental results show that FGCR significantly outperforms seven baseline models. Ablation study and user group analysis validate the effectiveness of each component in FGCR, particularly in modeling active users and sparse contextual information.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Min Gao, Jian-Yu Li, Chun-Hua Chen, Yun Li, Jun Zhang, Zhi-Hui Zhan
Summary: In this study, an enhanced MKR (EMKR) approach is proposed to address the two difficult issues in knowledge graph-based recommender systems. The attention mechanism and relation-aware graph convolutional neural network are utilized to capture users' historical behavior patterns and deep multi-relation semantic information. Additionally, a two-part modeling strategy is introduced for better representation of users in datasets with different sparsity. Experimental results show that EMKR outperforms state-of-the-art approaches, especially in sparse user-item interactions.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
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)
