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  • × author_ss:"Wu, I.-C."
  • × type_ss:"a"
  1. Wu, I.-C.; Liu, D.-R.; Chang, P.-C.: Learning dynamic information needs : a collaborative topic variation inspection approach (2009) 0.04 
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    Abstract
    For projects in knowledge-intensive domains, it is crucially important that knowledge management systems are able to track and infer workers' up-to-date information needs so that task-relevant information can be delivered in a timely manner. To put a worker's dynamic information needs into perspective, we propose a topic variation inspection model to facilitate the application of an implicit relevance feedback (IRF) algorithm and collaborative filtering in user modeling. The model analyzes variations in a worker's task-needs for a topic (i.e., personal topic needs) over time, monitors changes in the topics of collaborative actors, and then adjusts the worker's profile accordingly. We conducted a number of experiments to evaluate the efficacy of the model in terms of precision, recall, and F-measure. The results suggest that the proposed collaborative topic variation inspection approach can substantially improve the performance of a basic profiling method adapted from the classical RF algorithm. It can also improve the accuracy of other methods when a worker's information needs are vague or evolving, i.e., when there is a high degree of variation in the worker's topic-needs. Our findings have implications for the design of an effective collaborative information filtering and retrieval model, which is crucial for reusing an organization's knowledge assets effectively.
    Date
    2. 2.2010 19:22:51
  2. Wu, I.-C.; Niu, Y.-F.: Effects of anchoring process under preference stabilities for interactive movie recommendations (2015) 0.01 
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  3. Wu, I.-C.; Vakkari, P.: Effects of subject-oriented visualization tools on search by novices and intermediates (2018) 0.01 
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    Date
    9.12.2018 16:22:25

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