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  • × author_ss:"Zhao, Y."
  1. Bu, Y.; Ding, Y.; Xu, J.; Liang, X.; Gao, G.; Zhao, Y.: Understanding success through the diversity of collaborators and the milestone of career (2018) 0.05
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    Abstract
    Scientific collaboration is vital to many fields, and it is common to see scholars seek out experienced researchers or experts in a domain with whom they can share knowledge, experience, and resources. To explore the diversity of research collaborations, this article performs a temporal analysis on the scientific careers of researchers in the field of computer science. Specifically, we analyze collaborators using 2 indicators: the research topic diversity, measured by the Author-Conference-Topic model and cosine, and the impact diversity, measured by the normalized standard deviation of h-indices. We find that the collaborators of high-impact researchers tend to study diverse research topics and have diverse h-indices. Moreover, by setting PhD graduation as an important milestone in researchers' careers, we examine several indicators related to scientific collaboration and their effects on a career. The results show that collaborating with authoritative authors plays an important role prior to a researcher's PhD graduation, but working with non-authoritative authors carries more weight after PhD graduation.
  2. Zhao, Y.; Ma, F.; Xia, X.: Evaluating the coverage of entities in knowledge graphs behind general web search engines : Poster (2017) 0.01
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  3. Wu, Z.; Lu, C.; Zhao, Y.; Xie, J.; Zou, D.; Su, X.: ¬The protection of user preference privacy in personalized information retrieval : challenges and overviews (2021) 0.01
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  4. Ma, N.; Guan, J.; Zhao, Y.: Bringing PageRank to the citation analysis (2008) 0.01
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    Date
    31. 7.2008 14:22:05
  5. Huang, T.; Nie, R.; Zhao, Y.: Archival knowledge in the field of personal archiving : an exploratory study based on grounded theory (2021) 0.01
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    Date
    22. 1.2021 14:20:27
  6. Zhang, J.; Zhao, Y.: ¬A user term visualization analysis based on a social question and answer log (2013) 0.01
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    Abstract
    The authors of this paper investigate terms of consumers' diabetes based on a log from the Yahoo!Answers social question and answers (Q&A) forum, ascertain characteristics and relationships among terms related to diabetes from the consumers' perspective, and reveal users' diabetes information seeking patterns. In this study, the log analysis method, data coding method, and visualization multiple-dimensional scaling analysis method were used for analysis. The visual analyses were conducted at two levels: terms analysis within a category and category analysis among the categories in the schema. The findings show that the average number of words per question was 128.63, the average number of sentences per question was 8.23, the average number of words per response was 254.83, and the average number of sentences per response was 16.01. There were 12 categories (Cause & Pathophysiology, Sign & Symptom, Diagnosis & Test, Organ & Body Part, Complication & Related Disease, Medication, Treatment, Education & Info Resource, Affect, Social & Culture, Lifestyle, and Nutrient) in the diabetes related schema which emerged from the data coding analysis. The analyses at the two levels show that terms and categories were clustered and patterns were revealed. Future research directions are also included.