Article
Computer Science, Information Systems
Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
Summary: Personalized news recommendation is crucial for users to find interesting news information and alleviate information overload. This article provides a comprehensive overview of personalized news recommendation, including techniques for addressing core problems, challenges, public datasets, evaluation methods, and ways to improve the responsiveness of recommender systems. It also suggests future research directions.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Social Sciences, Interdisciplinary
Zijun Mao, Qi Zou, Tingting Bu, Ying Dong, Rongxiao Yan
Summary: This study investigates the relationship between service quality and continuance intention of government APPs, and finds that overall service quality has a positive impact on continuance intention through expectation confirmation, perceived usefulness, and user satisfaction.
Article
Computer Science, Information Systems
Iris Reychav, Ankur Arora, Rajiv Sabherwal, Karina Polyak, Jun Sun, Joseph Azuri
Summary: The study examined the use of mobile devices by patients to self-report medical data, finding that patient's effort in self-disclosure and expectation of feedback positively influenced feedback quality and confirmation. The results also showed a pathway from satisfaction to intention to disclose medical data through mobile technology.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Jiang Bian, Jizhou Huang, Shilei Ji, Yuan Liao, Xuhong Li, Qingzhong Wang, Jingbo Zhou, Dejing Dou, Yaqing Wang, Haoyi Xiong
Summary: This article introduces Feynman, an advertising platform based on federated learning, which aims to improve the conversion rate of mobile app recommendations. By utilizing advertisers' user records and building predictive models, Feynman has successfully helped several mobile apps in China attract over 100 million users and outperformed other plans in key metrics.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Social Sciences, Interdisciplinary
Yuke Huang, Zhiyuan Yu
Summary: This paper utilizes the expectation confirmation theory to establish a conceptual model that explores the factors influencing online users' continuous intention of watching news reported by AI anchors. The results show that the overall continuous intention is positive but not robust, with satisfaction, perceived intelligence, and trust directly predicting continuous intention. Expectation confirmation, perceived anthropomorphism, and perceived attractiveness influence continuous intention through the mediation of satisfaction. The proposed model explains 80.1% of the variance in continuous intention.
Article
Engineering, Civil
Bin Cao, Jianwei Zhao, Zhihan Lv, Peng Yang
Summary: Researched diversified recommendation problems in the Internet of Vehicles and constructed a multi-objective recommendation model, considering not only recommendation precision but also recommendation novelty and coverage.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Cybernetics
Saurabh Sharma, Yanxia Cheng, Prashant Sharma
Summary: This study assessed the determinants of continuous use intention (CUI) of news apps in India and found that personalization moderates the relationship between performance expectancy and habit with CUI.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2023)
Article
Computer Science, Information Systems
Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, W. Bruce Croft
Summary: This article addresses two research problems related to developing effective personal mobile assistants: target apps selection and recommendation. By leveraging context-aware models and a large dataset of mobile queries, proposed context-aware neural models take into account user behaviors and personal preferences to significantly outperform baseline models.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Xiu-Ming Loh, Voon-Hsien Lee, Lai-Ying Leong
Summary: This study analyzed data from 377 mobile payment users using a tri-level approach and established a research model for explaining mobile continuance intention. The study found that mobile complementarity is a fundamental antecedent of continuance intention.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Multidisciplinary
Zhu Shilin
Summary: This study proposes a recommendation model for news and media education resources based on a user model, which effectively captures the dynamic changes in user interests and demonstrates better performance in experiments.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Yufan Han
Summary: This study proposes an event network-oriented personalized news recommendation algorithm, which analyzes and predicts users' interests and preferences to build a personalized news recommendation model. The algorithm achieves high recall and coverage, and demonstrates good recommendation performance.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Junjie Wang, Ye Yang, Song Wang, Chunyang Chen, Dandan Wang, Qing Wang
Summary: Crowdsourced software testing, also known as crowdtesting, is a specialized form of crowdsourcing that requires skilled and dedicated crowdworkers. This paper addresses the issue of inappropriate task selection in crowdtesting, which leads to unpaid and wasted effort. The authors propose a context-aware personalized task recommendation approach called PTRec, which leverages a testing context model and a learning-based recommendation model to help crowdworkers make informed decisions. The evaluation of PTRec on a large crowdtesting platform demonstrates its potential in improving bug detection efficiency and increasing crowdworkers' earnings.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Business
Silas Formunyuy Verkijika, Brownhilder Ngek Neneh
Summary: Mobile payment systems have huge potential as alternative payment solutions, but their diffusion has been less than optimal in recent years. User recommendations play a crucial role in influencing the adoption of mobile payment technology, with positive recommendations encouraging usage and negative recommendations increasing resistance.
JOURNAL OF RETAILING AND CONSUMER SERVICES
(2021)
Article
Computer Science, Information Systems
Lei Li, Yongfeng Zhang, Li Chen
Summary: Providing user-understandable explanations to justify recommendations can enhance users' understanding of recommended items, improve system usability, and build trust. This study explores the use of pre-trained Transformer models in natural language generation for explainable recommendation, and proposes two solutions for effectively incorporating user and item IDs into these models. The experiments demonstrate that the continuous prompt learning approach, combined with sequential tuning and recommendation as regularization training strategies, consistently outperforms strong baselines on three datasets.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Communication
Aske Kammer
Summary: This article presents a longitudinal analysis of resource exchanges in the use of mobile news apps between news organizations and third-parties. The findings show that the prevalence and complexity of resource exchanges increase over time, with Google dominating the third-party network.
DIGITAL JOURNALISM
(2023)
Article
Computer Science, Artificial Intelligence
Yanwu Yang, Bernard J. Jansen, Yinghui Yang, Xunhua Guo, Daniel Zeng
IEEE INTELLIGENT SYSTEMS
(2019)
Article
Computer Science, Information Systems
Mingyue Zhang, Xuan Wei, Xunhua Guo, Guoqing Chen, Qiang Wei
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2019)
Article
Computer Science, Information Systems
Qun Chen, Ji-Wen Li, Jian-Guo Liu, Jing-Ti Han, Yun Shi, Xun-Hua Guo
Summary: This study investigates the impact of individual experience on learning outcomes in the context of online borrowing behavior. The findings suggest that while both prior successes and failures can motivate borrowers to engage in subsequent borrowing, only successes lead to desirable outcomes. The study also highlights the beneficial interaction effect between borrower's prior successes and failures on subsequent borrowing success.
INFORMATION SYSTEMS FRONTIERS
(2021)
Article
Operations Research & Management Science
Xunhua Guo, Yuejun Wang, Liang Huang, Jichen Li
Summary: Previous studies have shown that consumer preferences can be influenced by the ratings presented by recommender systems. However, this study discovers that in addition to the assimilation effects, contrast effects can also shift consumer preferences in the opposite direction. The hypotheses are validated through a laboratory experiment, offering valuable insights for improving the design and use of recommender systems.
JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING
(2022)
Article
Computer Science, Cybernetics
Xunhua Guo, Lingli Wang, Mingyue Zhang, Guoqing Chen
Summary: Research on recommender systems highlights the importance of item ranking in the algorithms' performance. To go beyond the traditional approach, this study examines the cognitive processes of online consumers when evaluating products in sequence. The results reveal that presenting the most recommended product in the second place enhances consumer purchase intentions and willingness to pay, providing insights for improving recommender system design.
ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION
(2023)
Article
Operations Research & Management Science
Benjiang Lu, Xunhua Guo, Nianlong Luo, Xueli Wang, Guoqing Chen
Summary: This paper explores the impact of employees' social relationships on intra-organizational social media platforms on their idea generation quantity and quality. Through empirical testing, it is found that employees' online social relationships are associated with group identification and proactive creativity, and only proactive creativity has a significant impact on idea generation quality.
JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING
(2022)
Article
Engineering, Manufacturing
Lingli Wang, Ni Huang, Yili Hong, Luning Liu, Xunhua Guo, Guoqing Chen
Summary: Voice-based AI systems replace traditional IVR systems in call center customer service, temporarily increasing machine service duration and demand for human service, but persistently reducing customer complaints. For complex service requests, customers learn from prior AI interactions, leading to fewer complaints. Speech-recognition failures increase demand for human service and customer complaints.
PRODUCTION AND OPERATIONS MANAGEMENT
(2023)
Article
Information Science & Library Science
Xixi Li, Zhijie Li, Qian Wang, Xunhua Guo
Summary: This study examines the direct and indirect influences of expressive and instrumental social ties between buyers and sellers in social media on buyers' purchase intention. The findings show that expressive social ties have a direct negative impact on purchase intention, while instrumental social ties have a positive impact on purchase intention.
INFORMATION TECHNOLOGY & PEOPLE
(2023)
Article
Information Science & Library Science
Ni Huang, Lingli Wang, Yili Hong, Lihui Lin, Xunhua Guo, Guoqing Chen
Summary: Online learners often lack sustained motivation in self-paced online learning, leading to inefficiency and high dropout rates. This study proposes that starting learning sessions at on-the-hour time points significantly influences learning outcomes. The study shows that on-the-hour time points activate users' implemental mindset, leading to greater learning persistence and better performance. Social presence, however, attenuates the effects of on-the-hour time points in online learning. These findings contribute to the design of online learning systems and provide implications for notification and reminder strategies to enhance effectiveness.
INFORMATION SYSTEMS RESEARCH
(2023)
Article
Information Science & Library Science
Qiang Wei, Yao Mu, Xunhua Guo, Weijie Jiang, Guoqing Chen
Summary: This study proposes a novel machine learning approach called multistage dynamic Bayesian network (MS-DBN) to address the challenges of diverse behaviors, variability in interest shifts, and the identification of psychological dynamics in recommender systems. By analyzing consumer shopping journeys from a marketing funnel perspective, MS-DBN models the generative processes of consumers' interactive behaviors with products considering stage transitions and interest shifts. The approach demonstrates superior performance over baseline methods in large-scale real-world data and comprehensive robustness checks.
INFORMATION SYSTEMS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Xunhua Guo, Guoqing Chen, Cong Wang, Qiang Wei, Zunqiang Zhang
Summary: The study proposes a novel approach utilizing iterative Bayesian distribution estimation technique to more accurately measure the helpfulness levels of reviews. Experimental results show that this approach outperforms existing methods in accuracy measures. Furthermore, results from user studies also demonstrate the predictive power of the new approach.
INFORMS JOURNAL ON COMPUTING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Cong Wang, Guoqing Chen, Qiang Wei, Guannan Liu, Xunhua Guo
FUZZY TECHNIQUES: THEORY AND APPLICATIONS
(2019)
Article
Computer Science, Information Systems
Yumei He, Xunhua Guo, Guoqing Chen
JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS
(2019)
Article
Business
Kai Reimers, Xunhua Guo, Mingzhi Li
ELECTRONIC MARKETS
(2019)
