Article
Computer Science, Information Systems
Tuukka Ruotsalo, Sean Weber, Krzysztof Z. Gajos
Summary: This article introduces an active tag recommendation approach for interactive entity search, which suggests tags based on user interactions. The approach utilizes online reinforcement learning and rewards or penalizes the model based on user interactions. The approach is evaluated in task-based user experiments and the results show that it improves entity ranking, increases interaction effectiveness, reduces the need for writing queries, and does not compromise task execution time.
INFORMATION PROCESSING & MANAGEMENT
(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, Information Systems
Wenjie Wang, Ling-Yu Duan, Hao Jiang, Peiguang Jing, Xuemeng Song, Liqiang Nie
Summary: This study introduces Market2Dish, which aims to achieve personalized health-aware food recommendation through recipe retrieval, user health profiling, and health-aware food recommendation. By capturing health-related information from social networks and utilizing a deep model to learn the correlations between users and recipes, the proposed scheme offers better food recommendations.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jiameng Gao, Chunxia Zhang, Yanyan Xu, Meiqiu Luo, Zhendong Niu
Summary: This paper proposes a hybrid microblog recommendation approach based on a deep neural network, incorporating user interest tags and topics, describing candidate microblogs with features and utilizing collaborative filtering to obtain recommended microblogs. Experimental results demonstrate that the proposed method significantly outperforms existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Sreekanth Madisetty, Maunendra Sankar Desarkar
Summary: A two-phase approach is proposed to retrieve tweets related to planned events on Twitter, using different scoring mechanisms to obtain the final score. Experimental results show that the method outperforms baseline and literature methods for both benchmark datasets.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Areej Bin Suhaim, Jawad Berri
Summary: This research proposes a user-based collaborative filtering recommendation technique for social networks that utilizes the context and characteristics of social networks to recommend personalized content to users. The approach streamlines contextual features and social network data to reduce data dimensionality and uses learning techniques to uncover contextual information for ranking tweets. The proposed approach outperformed other algorithms in recommending tweets, achieving higher accuracy by appropriately handling the fine details of the user's context.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Pengyu Xu, Mingxuan Xia, Lin Xiao, Huafeng Liu, Bing Liu, Liping Jing, Jian Yu
Summary: Tag recommendation improves the quality of information retrieval services by assisting users in tagging. However, existing studies rarely consider the long-tail distribution of tags and the topic-tag correlation. This paper proposes a Topic-Guided Tag Recommendation (TGTR) model that incorporates dynamic neural topics to recommend tags and balances the effects of topics and tags. Experimental results show that our model outperforms state-of-the-art approaches, especially on tail-tags.
Article
Computer Science, Artificial Intelligence
Beilun Wang, Haoqing Xu, Chunshu Li, Yuchen Li, Meng Wang
Summary: This paper proposes a Knowledge-enhanced Tag-aware Recommendation System (KTRS) that incorporates auxiliary knowledge to improve the performance of tag-aware recommendation systems. Experimental results demonstrate that the proposed system outperforms other recommendation methods on real-world datasets, highlighting the effectiveness of auxiliary knowledge.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Triyanna Widiyaningtyas, Indriana Hidayah, Teguh B. Adji
Summary: A new User Profile Correlation-based Similarity (UPCSim) algorithm is proposed to improve recommendation system accuracy by considering user behavior and rating values, and using user profile data to find similarity weights. Compared to the previous algorithm, the UPCSim algorithm outperforms in recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.
JOURNAL OF BIG DATA
(2021)
Article
Computer Science, Artificial Intelligence
Jihu Wang, Yuliang Shi, Han Yu, Zhongmin Yan, Hui Li, Zhenjie Chen
Summary: Leveraging knowledge graphs (KGs) via graph convolutional networks (GCNs) to enhance recommender systems has gained considerable attention. However, existing approaches fail to consider entity pairs without relations, which may possess important information. To address this, we propose a novel relation-aware attentional GCN (RAAGCN) that aggregates entity pairs with and without explicit relations and distinguishes the importance of relational context information. Based on RAAGCN, we propose a user preference and item attractiveness capturing model (UPIACM) that decomposes user preference into interest and rating preferences and incorporates item attractiveness. Our model outperforms state-of-the-art baseline methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Business
Hangzhou Yang, Huiying Gao
Summary: Online health communities (OHCs) are platforms that help health consumers communicate and obtain social support. However, finding appropriate peers for support exchange is difficult due to the large user base. This study proposes a novel user recommendation method that utilizes social information in OHCs, outperforming baseline models. Incorporating social information significantly improves the recommender system's performance and can enhance communication among community members.
Article
Computer Science, Information Systems
Lifeng Han, Li Chen, Xiaolong Shi
Summary: Personalized recommendation has gained attention in academia and industry for minimizing information overload and producing good results. Social recommendation models that utilize user trust relationships effectively have been found to solve common problems in traditional collaborative filtering algorithms, such as data sparsity and cold start. However, existing models have overlooked indirect trust relationships and item correlations. To address these issues, the proposed probabilistic matrix factorization-based recommendation model considers direct and indirect trust relationships, user preference similarities, and item correlations. Evaluation on FilmTrust and CiaoDVD datasets demonstrates that the model alleviates the user's cold start problem and provides higher accuracy and diversity in recommendations compared to popular algorithms.
Article
Computer Science, Information Systems
Xumin Chen, Ruobing Xie, Zhijie Qiu, Peng Cui, Ziwei Zhang, Shukai Liu, Shiqiang Yang, Bo Zhang, Leyu Lin
Summary: In social-enhanced recommendation systems, user-group-based social diffusion plays a crucial role in broadcasting information to target user groups. However, most systems overlook the importance of modeling and predicting the social diffusion module. This study proposes a novel Group-based social diffusion (GSD) model that optimizes click, share, and return stages to improve recommendation performance, and achieves significant improvements in experiments.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Li Li, Zhongqun Wang, Chen Li, Linjun Chen, Yong Wang
Summary: In this study, a novel collaborative filtering recommendation technique (CFR-F) is proposed to defend against shilling attacks. Experimental results demonstrate that the approach can recommend accurate information resources with lower Mean Absolute Error (MAE) and Average Prediction Shift (APS) compared to traditional techniques.
CONNECTION SCIENCE
(2022)
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
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)