Noise-Averse Combination Method

机译：噪声厌恶组合法

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This paper proposes a new method for combining information in recommender systems based on Dempster-Shafer theory. Within this method, focal elements whose probabilities are less than or equal to an infinitesimal threshold are considered as noise that may be caused by the process of fusing information, and then eliminated. Comparing with two baselines, known as 2-points focused and 2-probabilities focused combination methods, the new method is more effective because it does not eliminate focal elements whose probabilities are not very small. Moreover, the new method is tested in a wide range of experiments on MovieLens data set, and the experimental results indicate that the proposed method is much better than the baselines in accuracy of recommendations.

机译：本文提出了一种基于Dempster-Shafer理论的推荐系统中信息融合的新方法。在此方法中，概率小于或等于无穷大阈值的焦点元素被视为可能是由于融合信息的过程而导致的噪声，然后将其消除。与称为2点聚焦和2概率聚焦的组合方法的两个基线相比，新方法更有效，因为它不会消除概率不是很小的聚焦元素。此外，该新方法在MovieLens数据集上进行了广泛的实验测试，实验结果表明，该方法在推荐准确性方面要比基线好得多。

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《IEEE International Conference on Tools with Artificial Intelligence》|2016年|86-90|共5页
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Van-Doan Nguyen; Van-Nam Huynh;
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Time complexity; Cognition; Motion pictures; Recommender systems; Context; Conferences; Artificial intelligence;

机译：时间复杂度认知运动图片推荐系统上下文会议人工智能;

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Noise-Averse Combination Method

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