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  • × author_ss:"Liu, Y."
  • × year_i:[2020 TO 2030}
  1. Liu, Y.; Qin, C.; Ma, X.; Liang, H.: Serendipity in human information behavior : a systematic review (2022) 0.00 
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    Abstract
    Purpose Serendipitous information discovery has become a unique and important approach to discovering and obtaining information, which has aroused a growing interest for serendipity in human information behavior. Despite numerous publications, few have systematically provided an overview of current state of serendipity research. Consequently, researchers and practitioners are less able to make effective use of existing achievements, which limits them from making advancements in this domain. Against this backdrop, we performed a systematic literature review to explore the world of serendipity and to recapitulate the current states of different research topics. Design/methodology/approach Guided by a prior designed review protocol, this paper conducted both automatic and manual search for available studies published from January 1990 to December 2020 on seven databases. A total of 207 serendipity studies closely related to human information behavior form the literature pool. Findings We provide an overview of distinct aspects of serendipity, that is research topics, potential benefits, related concepts, theoretical models, contextual factors and data collection methods. Based on these findings, this review reveals limitations and gaps in the current serendipity research and proposes an agenda for future research directions. Originality/value By analyzing current serendipity research, developing a knowledge framework and providing a research agenda, this review is of significance for researchers who want to find new research questions or re-align current work, for beginners who need to quickly understand serendipity, and for practitioners who seek to cultivate serendipity in information environments.
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  2. Fang, Z.; Liu, Y.; Jiang, F.; Dong, W.: How does family support influence digital immigrants' extended use of smartphones? : an empirical study based on IT identity theory (2023) 0.00 
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    Abstract
    The number of digital immigrants using new technologies such as smartphones is rapidly increasing. However, digital immigrants still struggle to actually use and benefit from digital technology. This article examines the role of family support in digital immigrants' use of more smartphone functions based on information technology (IT) identity theory. We surveyed 241 digital immigrants who owned smartphones and used structural equation modeling (PLS-SEM) for analysis. We examined the contributing roles of family support for digital immigrants' IT identity and extended use behavior. Family cognitive and emotional support can shape IT identity by improving the smartphone-related experience. Family support has a positive impact on digital immigrants' self-efficacy, embeddedness, perceived usefulness, and perceived enjoyment of using a smartphone. Positive usage experience can also facilitate the establishment of IT identity, which is a key predictor of smartphone use behavior. A strong IT identity also promotes extended use behavior. We discuss the contributions and implications of our findings.
    Type
    a
  3. Sun, J.; Zhu, M.; Jiang, Y.; Liu, Y.; Wu, L.L.: Hierarchical attention model for personalized tag recommendation : peer effects on information value perception (2021) 0.00 
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    Abstract
    With the development of Web-based social networks, many personalized tag recommendation approaches based on multi-information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user-item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real-world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.
    Type
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  4. Tang, X.-B.; Fu, W.-G.; Liu, Y.: Knowledge big graph fusing ontology with property graph : a case study of financial ownership network (2021) 0.00 
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    Abstract
    The scale of knowledge is growing rapidly in the big data environment, and traditional knowledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a knowledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the knowledge representation model is called knowledge big graph. In addition, this paper applies the knowledge big graph model to the ownership network in the China's financial field and builds a financial ownership knowledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership knowledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the knowledge big graph could provide technical reference for big data knowledge organization and services.
    Type
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  5. Qin, C.; Liu, Y.; Ma, X.; Chen, J.; Liang, H.: Designing for serendipity in online knowledge communities : an investigation of tag presentation formats and openness to experience (2022) 0.00 
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