多维度等级评分模型优化技术

Optimizations of Multi-Aspect Rating Inference Model

研究了多维度等级评分模型的训练学习优化技术.为了解决不同用户之间的评分标注所存在的不一致性,提出两种简单、有效的模型训练优化技术,包括基于容忍度的样本选择方法和基于排序损失的样本选择方法.另外,为了充分利用不同特征的用户评分标注之间的相关性,提出了一个面向属性的协同过滤技术以改善多维度等级评分模型.在两个公开的英语和汉语真实餐馆评论数据集上进行实验验证,实验结果表明,所提出的方法有效地改善了等级评分的性能.

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.