Optimizations of Multi-Aspect Rating Inference Model
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    Abstract:

    This paper addresses an issue of training optimization of multi-aspect rating inference. First, to address the issue of author inconsistency rating annotation, this paper proposes two simple approaches to improving the standard rating inference models by optimizing sample selection for training, including tolerance-based selection and ranking-loss-based selection methods. Second, to explore correlations between ratings across a set of aspects, this paper presents an aspect-oriented collaborative filtering technique to improve rating inference models. Experiments on two publicly available English and Chinese restaurant review data sets have demonstrated significant improvements over standard algorithms.

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王会珍,朱靖波.多维度等级评分模型优化技术.软件学报,2013,24(7):1545-1556

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History
  • Received:September 29,2011
  • Revised:February 15,2012
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  • Online: July 02,2013
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