基于改进在线支持向量机模型的微博过滤技术研究

Hui Ning, Song Li, Fanhu Zeng, Li Xu
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引用次数: 0

摘要

随着微博数量的快速增长,大量无用信息充斥在用户的视野中,用户难以根据个人兴趣选择微博推荐服务,因此将微博过滤技术应用到微博服务中。用户的兴趣随着时间的变化而变化,传统的批量学习已经不能满足用户兴趣模型的需求。而基于在线学习的机器学习在一定程度上解决了这些问题。微博过滤的优化可以减少分类错误,提高数据的分类能力,但也有其自身的缺点。尽管在线支持向量机过滤非常出色,但也存在长期存在的缺点。本文主要通过减少训练集的大小、训练次数和迭代次数来提高在线支持向量机过滤的效率。实验结果表明,滤波性能波动较小,但由于其效率优势几乎可以忽略不计,且数据量越大,效率越明显。
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Research on microblog filtering technology based on improved online support vector machine model
With the rapid growth of the number of micro-blog, a lot of useless information flooded in the users vision, the users find it difficult to choose micro-blog recommendation service according to personal interest, so micro-blog filtering technology is applied to the micro-blog service. The interests of users change with the time, so the traditional batch learning can not be able to satisfy the need of users interest model. However, the machine learning which is based on online learning solved these problem in a certain way. The optimization of micro-blog filtering can decrease classification errors and improve the classification of data, but it has its own disadvantages. Although online support vector machine filtration is excellent, there's long-running shortcomings. The paper focus on the improvement of efficiency of the online support vector machine filtering by reducing the size of the training set, the number of training and the number of iterations. The experimental results show that the filtering performance fluctuate slightly, but it can almost be ignored because of the advantage of its efficiency, and the great amount of data is, the greater the efficiency becomes more obvious.
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