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  • × author_ss:"Sun, J."
  • × year_i:[2020 TO 2030}
  1. 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.
  2. Min, C.; Chen, Q.; Yan, E.; Bu, Y.; Sun, J.: Citation cascade and the evolution of topic relevance (2021) 0.00 
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    Abstract
    Citation analysis, as a tool for quantitative studies of science, has long emphasized direct citation relations, leaving indirect or high-order citations overlooked. However, a series of early and recent studies demonstrate the existence of indirect and continuous citation impact across generations. Adding to the literature on high-order citations, we introduce the concept of a citation cascade: the constitution of a series of subsequent citing events initiated by a certain publication. We investigate this citation structure by analyzing more than 450,000 articles and over 6 million citation relations. We show that citation impact exists not only within the three generations documented in prior research but also in much further generations. Still, our experimental results indicate that two to four generations are generally adequate to trace a work's scientific impact. We also explore specific structural properties-such as depth, width, structural virality, and size-which account for differences among individual citation cascades. Finally, we find evidence that it is more important for a scientific work to inspire trans-domain (or indirectly related domain) works than to receive only intradomain recognition in order to achieve high impact. Our methods and findings can serve as a new tool for scientific evaluation and the modeling of scientific history.

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