Article
Computer Science, Software Engineering
Xinqiang Xie, Xiaochun Yang, Bin Wang, Qiang He
Summary: The researchers propose a novel multi-relationship embedded approach for software developer recommendation, where they define multiple relationships, learn vector representations of developers and tasks, encode relationships into the embedding process, propagate embeddings from high-order connectivity using graph convolution network, generate attentive weights based on attention mechanism, and outperform other five state-of-the-art approaches significantly in experiments.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Information Systems
Dan Wu, Shu Fan, Fang Yuan
Summary: This paper uses ResearchGate to explore how users seek out experts on ASNS through pathways, and the results indicate that users mainly access profile pages, search pages, and publication pages, with low utilization of the latter.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Chemistry, Multidisciplinary
Kyoungsoo Bok, Heesub Song, Dojin Choi, Jongtae Lim, Deukbae Park, Jaesoo Yoo
Summary: The study proposes a method for recommending experts based on social activity analysis, identifying experts' influence by analyzing user interests, relationships, and response quality, and enhancing recommendation accuracy by matching expert groups.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Jie Liao, Wei Zhou, Fengji Luo, Junhao Wen, Min Gao, Xiuhua Li, Jun Zeng
Summary: This paper proposes a new social recommendation system called SocialLGN, which addresses the challenges of accurately learning user and item representations from user-item interaction graphs and social graphs, as well as integrating user representations learned from these two graphs. Experimental results demonstrate the superiority of SocialLGN, especially in handling the cold-start problem.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Xiaolin Zheng, Yanchao Tan, Yan Wang, Xiangyu Wei, Shengjia Zhang, Chaochao Chen, Longfei Li, Carl Yang
Summary: The sparse interactions between users and items on the web have made it difficult to represent them in recommender systems. Existing approaches use item attributes to alleviate the data sparsity problem, but manual labeling of attribute quality is time-consuming. In response, HQRec is proposed to automatically measure attribute quality and make accurate recommendations. HQRec achieves significant performance gains over state-of-the-art baselines, with an average improvement of 14.73% in terms of Recall and NDCG metrics.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Automation & Control Systems
Le Wu, Peijie Sun, Richang Hong, Yong Ge, Meng Wang
Summary: The paper proposes a collaborative neural social recommendation (CNSR) model that combines the social embedding part and the collaborative neural recommendation (CNR) part, successfully addressing the challenges in social recommendation and demonstrating high recommendation effectiveness.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Software Engineering
Viral Sheth, Kostadin Damevski
Summary: Stack Overflow is a widely used question and answer forum for developers worldwide, but valuable comments on the platform are often difficult to locate. Recent research has found that the comment display mechanism hides important and relevant comments, making it hard for readers to understand the context. This paper proposes a machine learning-based technique to improve this issue by identifying comment relatedness and clustering.
AUTOMATED SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Hardware & Architecture
Wen Zhang, Jiangpeng Zhao, Rui Peng, Song Wang, Ye Yang
Summary: The sustainability of open source projects is crucial for their long-term and reliable development. This article proposes a novel approach called SusRec to address the issue of inexperienced developers having limited opportunities to resolve bugs. By utilizing multimodal learning and ensemble learning, SusRec improves developer recommendations for bug reports without sacrificing accuracy, ultimately enhancing the sustainability of open source software projects.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Computer Science, Artificial Intelligence
Zohreh Fallahnejad, Hamid Beigy
Summary: The growing popularity of community question answering websites is evident from the increasing number of users. However, many existing methods for identifying talented users in these communities suffer from vocabulary mismatches. This paper proposes two translation methods that utilize an attention mechanism to extract more relevant translations. By using word attention scores, the proposed methods bridge the lexical gap and improve expert retrieval results. Extensive experiments demonstrate that the proposed methods outperform the best baseline method by up to 14.11/% MAP improvement.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematics
Bin Tang, Qinqin Gao, Xin Cui, Qinglong Peng, Xu Yu
Summary: CQA is a new knowledge-sharing platform that allows people to acquire knowledge and share experiences. Expert recommendation in CQA is important, but existing algorithms are not ideal for new platforms with sparse data. This paper proposes a cross-platform expert recommendation method that aligns features and achieves better results compared to other algorithms.
Article
Computer Science, Information Systems
Shuotong Bai, Lei Liu, Huaxiao Liu, Mengxi Zhang, Chenkun Meng, Peng Zhang
Summary: This paper aims to help GitHub users find recently like-minded developers to follow or reach out by leveraging their recent activities-Event Data. The study conducted an online survey and analyzed the preferences of GitHub users regarding following others with similar recent events. Based on the survey results, the study partitioned the events into three categories and developed a recommendation approach using vector similarity calculation, clustering, and deep learning models.
INFORMATION AND SOFTWARE TECHNOLOGY
(2022)
Article
Computer Science, Software Engineering
Gias Uddin, Foutse Khomh, Chanchal K. Roy
Summary: Stack Overflow is a popular online technical Q&A site among developers, providing support for coding and diverse development needs. Recent surveys have shown that developers consider the combination of code examples and reviews about APIs to be more useful than official API documentation, especially when official resources are incomplete, ambiguous, incorrect, or outdated.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Qiyao Peng, Wenjun Wangb, Hongtao Liu, Yinghui Wang, Hongyan Xu, Minglai Shao
Summary: In Community Question Answering (CQA) websites, expert finding aims to seek relevant experts for answering questions. This paper proposes an expert finding method with a multi-grained hierarchical matching framework, which has the potential to capture the comprehensive relevance between candidate experts and target questions.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zhenchun Duan, Weihong Xu, Yuantao Chen, Lin Ding
Summary: The recommendation system is a primary tool to tackle the issue of information overload, facing challenges such as data sparsity, cold start, and scalability. The ETBRec algorithm improves prediction accuracy by considering trust differences and expert definitions, with experimental results showing better performance on some evaluation metrics.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yujia Liu, Changyong Liang, Francisco Chiclana, Jian Wu
Summary: The trust relationship among experts in a networked group can be estimated using novel knowledge coverage-based trust propagation operators. By aggregating trust paths and penalizing trust decay, a complete trust network can be constructed. A recommendation mechanism combining subjective and objective weights allows experts to accept consensus recommendations based on trust.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Qian Li, Xiangmeng Wang, Zhichao Wang, Guandong Xu
Summary: In recommendation systems, the MNAR problem causes selection bias and affects recommendation performance. Existing approaches focus on modeling the exposure of missing entries but overlook the causal perspective. To address this, we propose a method called DENC (De-Bias Network Confounding in Recommendation) that provides causal analysis on MNAR using both inherent factors and auxiliary networks. DENC includes an exposure model to control confounding and a deconfounding model for balanced representation learning. Experiments on three datasets demonstrate that DENC outperforms state-of-the-art baselines.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Article
Computer Science, Information Systems
Abdul Rehman, Imran Razzak, Guandong Xu
Summary: Federated learning is a promising framework for preserving user data privacy in machine and deep learning models. This study proposes a novel framework based on federated learning to detect side-channel attacks in smartphone keyboard input, and experimental results demonstrate its effectiveness.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Zongda Wu, Jian Xie, Shigen Shen, Chongze Lin, Guandong Xu, Enhong Chen
Summary: In this article, a privacy protection solution is proposed for a Chinese keyword-based book search service. By modifying the user query sequence, the user query topics can be confused to protect the user's topic privacy on an untrusted server, while maintaining the accuracy of the book search service. A client-based framework and privacy model are introduced to guide the generation of cover queries based on a user query sequence. The proposed modification algorithm quickly generates cover queries that meet the privacy model by replacing, deleting, and adding keywords. The effectiveness of the approach is demonstrated through theoretical analysis and experimental evaluation, showing improved security for users' topic privacy without compromising efficiency, accuracy, and usability of the existing Chinese keyword book search service, thus contributing to the construction of a privacy-preserving text retrieval platform in an untrusted network environment.
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
(2023)
Editorial Material
Computer Science, Information Systems
Hongzhi Yin, Yizhou Sun, Guandong Xu, Evangelos Kanoulas
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Editorial Material
Computer Science, Information Systems
Imran Razzak, Muhammad Khuram Khan, Guandong Xu, Fahmi Khalifa
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Editorial Material
Computer Science, Information Systems
Hongzhi Yin, Yizhou Sun, Guandong Xu, Evangelos Kanoulas
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Tri Dung Duong, Qian Li, Guandong Xu
Summary: Counterfactual fairness addresses discrimination between model predictions in the actual and counterfactual worlds. This research proposes a minimax game-theoretic model that achieves counterfactual fairness without strict assumptions on structural causal models.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Anchen Li, Bo Yang, Huan Huo, Hongxu Chen, Guandong Xu, Zhen Wang
Summary: Recently, deep learning techniques have achieved great success in recommender systems. However, most deep methods lack explicit extraction of mutual semantic relationships between users and items, which are latent in user-item interactions. Additionally, these methods primarily focus on representation learning in euclidean geometry, ignoring the non-euclidean latent anatomy of the bipartite graph structure. This work presents HNCR, a deep hyperbolic representation learning method that leverages mutual semantic relationships for collaborative filtering tasks, demonstrating superior performance compared to euclidean counterparts and state-of-the-art recommendation baselines.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, Guandong Xu
Summary: This study proposes an unbiased and semantic-aware disentanglement learning approach from a causal perspective, which generates semantic-aware representations by disentangling users' true intents from specific item context, and designs a causal intervention mechanism to eliminate confounding bias.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Haoran Yang, Xiangyu Zhao, Muyang Li, Hongxu Chen, Guandong Xu
Summary: This paper investigates the implementation of differential privacy (DP) on graph edges and observes a decrease in performance. The authors propose leveraging graph contrastive learning to alleviate the performance drop resulting from DP.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Qian Li, Zhichao Wang, Shaowu Liu, Gang Li, Guandong Xu
Summary: Treatment effect estimation is crucial for answering questions about the impact of specific treatments. However, treatment assignment bias is a fundamental issue in this research. To address this problem, the paper proposes a novel causal optimal transport (CausalOT) model that utilizes global information on observational covariates to mitigate limited overlapping, and designs a new counterfactual loss to improve estimation accuracy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Review
Computer Science, Cybernetics
Li He, Guandong Xu, Shoaib Jameel, Xianzhi Wang, Hongxu Chen
Summary: This article introduces a graph-based model for detecting spam information in product reviews on e-commerce platforms. The model utilizes relevant metadata and relational data to capture semantic information in the review text, and synthesizes interactions at different levels through fusion techniques. Experimental results show that the proposed model outperforms other baselines.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Zhihong Cui, Xiangguo Sun, Hongxu Chen, Li Pan, Lizhen Cui, Shijun Liu, Guandong Xu
Summary: Nowadays, dynamic recommendations struggle to find user online interest evolving patterns due to their complex interactions. Each interaction is influenced by multiple underlying reasons, so it is necessary to analyze each interaction instance individually. Additionally, traditional sequential models fail to personalize recommendations for users with different long-term and short-term tastes. In this article, a novel recommendation model based on Graph Diffusion and Ebbinghaus Curve is proposed, which explores underlying reasons for interactions and captures users' personalized strategies using a well-designed neural network and the Ebbinghaus Curve. Extensive experiments on real-world datasets confirm the superiority of the proposed model over existing baselines.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Hadeel Alhosaini, Sultan Alharbi, Xianzhi Wang, Guandong Xu
Summary: This paper provides a comprehensive review of API recommendation research for mashup creation. It compares different approaches and techniques, identifies challenges in current research, and suggests promising directions for future studies. It serves as a useful reference for researchers and practitioners in this field.
Article
Automation & Control Systems
Yakun Chen, Ruotong Hu, Zihao Li, Chao Yang, Xianzhi Wang, Guodong Long, Guandong Xu
Summary: This study proposes a novel approach to deal with missing values in multivariate time series. The approach combines graph and recurrent neural networks to capture dependencies between variables and time. By integrating external data sources, such as domain knowledge and implicit relationships between nodes, the ability to impute missing values is enhanced. Experimental results show that the model outperforms current state-of-the-art baselines in various industrial fields.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
