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  • × author_ss:"Chen, L."
  • × year_i:[2010 TO 2020}
  1. Tang, X.; Chen, L.; Cui, J.; Wei, B.: Knowledge representation learning with entity descriptions, hierarchical types, and textual relations (2019) 0.01 
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    Date
    17. 3.2019 13:22:53
  2. Xie, H.; Li, X.; Wang, T.; Lau, R.Y.K.; Wong, T.-L.; Chen, L.; Wang, F.L.; Li, Q.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy (2016) 0.01 
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    Abstract
    In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.
  3. Chen, L.; Holsapple, C.W.; Hsiao, S.-H.; Ke, Z.; Oh, J.-Y.; Yang, Z.: Knowledge-dissemination channels : analytics of stature evaluation (2017) 0.01 
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    Abstract
    Understanding relative statures of channels for disseminating knowledge is of practical interest to both generators and consumers of knowledge flows. For generators, stature can influence attractiveness of alternative dissemination routes and deliberations of those who assess generator performance. For knowledge consumers, channel stature may influence knowledge content to which they are exposed. This study introduces a novel approach to conceptualizing and measuring stature of knowledge-dissemination channels: the power-impact (PI) technique. It is a flexible technique having 3 complementary variants, giving holistic insights about channel stature by accounting for both attraction of knowledge generators to a distribution channel and degree to which knowledge consumers choose to use a channel's knowledge content. Each PI variant is expressed in terms of multiple parameters, permitting customization of stature evaluation to suit its user's preferences. In the spirit of analytics, each PI variant is driven by objective evidence of actual behaviors. The PI technique is based on 2 building blocks: (a) power that channels have for attracting results of generators' knowledge work, and (b) impact that channel contents' exhibit on prospective recipients. Feasibility and functionality of the PI-technique design are demonstrated by applying it to solve a problem of journal stature evaluation for the information-systems discipline.

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