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  • × author_ss:"Yuan, X."
  1. Yuan, X.; Belkin, N.J.: Investigating information retrieval support techniques for different information-seeking strategies (2010) 0.01 
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    Abstract
    We report on a study that investigated the efficacy of four different interactive information retrieval (IIR) systems, each designed to support a specific information-seeking strategy (ISS). These systems were constructed using different combinations of IR techniques (i.e., combinations of different methods of representation, comparison, presentation and navigation), each of which was hypothesized to be well suited to support a specific ISS. We compared the performance of searchers in each such system, designated experimental, to an appropriate baseline system, which implemented the standard specified query and results list model of current state-of-the-art experimental and operational IR systems. Four within-subjects experiments were conducted for the purpose of this comparison. Results showed that each of the experimental systems was superior to its baseline system in supporting user performance for the specific ISS (that is, the information problem leading to that ISS) for which the system was designed. These results indicate that an IIR system, which intends to support more than one kind of ISS, should be designed within a framework which allows the use and combination of different IR support techniques for different ISSs.
  2. Yuan, X.; Belkin, N.J.: Evaluating an integrated system supporting multiple information-seeking strategies (2010) 0.01 
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    Abstract
    Many studies have demonstrated that people engage in a variety of different information behaviors when engaging in information seeking. However, standard information retrieval systems such as Web search engines continue to be designed to support mainly one such behavior, specified searching. This situation has led to suggestions that people would be better served by information retrieval systems which support different kinds of information-seeking strategies. This article reports on an experiment comparing the retrieval effectiveness of an integrated interactive information retrieval (IIR) system which adapts to support different information-seeking strategies with that of a standard baseline IIR system. The experiment, with 32 participants each searching on eight different topics, indicates that using the integrated IIR system resulted in significantly better user satisfaction with search results, significantly more effective interaction, and significantly better usability than that using the baseline system.
  3. Zhu, J.; Han, L.; Gou, Z.; Yuan, X.: ¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems (2018) 0.01 
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    Abstract
    Recommender systems are effective in predicting the most suitable products for users, such as movies and books. To facilitate personalized recommendations, the quality of item ratings should be guaranteed. However, a few ratings might not be accurate enough due to the uncertainty of user behavior and are referred to as natural noise. In this article, we present a novel fuzzy clustering-based method for detecting noisy ratings. The entropy of a subset of the original ratings dataset is used to indicate the data-driven uncertainty, and evaluation metrics are adopted to represent the prediction-driven uncertainty. After the repetition of resampling and the execution of a recommendation algorithm, the entropy and evaluation metrics vectors are obtained and are empirically categorized to identify the proportion of the potential noise. Then, the fuzzy C-means-based denoising (FCMD) algorithm is performed to verify the natural noise under the assumption that natural noise is primarily the result of the exceptional behavior of users. Finally, a case study is performed using two real-world datasets. The experimental results show that our proposal outperforms previous proposals and has an advantage in dealing with natural noise.

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