Search (4 results, page 1 of 1)

  • × author_ss:"Wu, M."
  1. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.00 
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    Abstract
    Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter-/cross-/multi-disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co-topic layer and a co-authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments-one with a local dataset and the other with a global dataset-demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.
    Date
    22. 6.2023 18:07:12
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.7, S.775-790
  2. Wu, M.; Fuller, M.; Wilkinson, R.: Using clustering and classification approaches in interactive retrieval (2001) 0.00 
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    Source
    Information processing and management. 37(2001) no.3, S.459-484
  3. Wu, M.; Turpin, A.; Thom, J.A.; Scholer, F.; Wilkinson, R.: Cost and benefit estimation of experts' mediation in an enterprise search (2014) 0.00 
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    Abstract
    The success of an enterprise information retrieval system is determined by interactions among three key entities: the search engine employed; the service provider who delivers, modifies, and maintains the engine; and the users of the service within the organization. Evaluations of an enterprise search have predominately focused on the effectiveness and efficiency of the engine, with very little analysis of user involvement in the process, and none on the role of service providers. We propose and evaluate a model of costs and benefits to a service provider when investing in enhancements to the ranking of documents returned by their search engine. We demonstrate the model through a case study to analyze the potential impact of using domain experts to provide enhanced mediated search results. By demonstrating how to quantify the cost and benefit of an improved information retrieval system to the service provider, our case study shows that using the relevance assessments of domain experts to rerank original search results can significantly improve the accuracy of ranked lists. Moreover, the service provider gains substantial return on investment and a higher search success rate by investing in the relevance assessments of domain experts. Our cost and benefit analysis results are contrasted with standard modes of effectiveness analysis, including quantitative (using measures such as precision) and qualitative (through user preference surveys) approaches. Modeling costs and benefits explicitly can provide useful insights that the other approaches do not convey.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.1, S.146-163
  4. Wu, M.; Hawking, D.; Turpin, A.; Scholer, F.: Using anchor text for homepage and topic distillation search tasks (2012) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.6, S.1235-1255

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