一种基于隐语义概率模型的个性化Web服务推荐方法

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	一种基于隐语义概率模型的个性化Web服务推荐方法
其他题名	A personalized Web service recommendation method based on latent semantic probabilistic model
	胡堰; 彭启民; 胡晓惠; Hu, Yan
	2014
发表期刊	计算机研究与发展
ISSN	10001239
卷号	51 期号:8 页码:1781-1793
摘要	为了满足Web服务使用者的个性化需求,提出了一种基于隐语义概率模型的用户指标偏好预测方法,用于个性化Web服务推荐.首先,引入两个决定用户指标偏好的关键因素:用户以及用户所处的服务情境,隐语义概率模型借助隐含类别建立用户指标偏好、用户及服务情境三者之间的隐含语义依赖关系,并且为描述用户、服务情境、指标偏好多方面的特征,允许这三者可同时以不同的概率隶属于多个隐含类别;然后,将期望极大(expectation maximization,EM)算法运用于由层次分析法获得的训练数据,以估计隐语义概率模型的参数;最后,使用该模型预测用户在特定服务情境下的指标偏好.隐语义概率模型与标准的基于内存的协同过滤以及基于聚类改进的协同过滤相比,不仅具有明确的数学模型,而且实验结果表明,隐语义概率模型对用户个性化指标偏好的预测精度最高,同时可以缓解数据稀疏性带来的不良影响.
收录类别	EI ; CSCD
其他摘要	In order to meet service users' personalized requirements, a latent semantic probabilistic model is proposed to predict users' criteria preferences for Web service recommendation. Users' criteria preferences are mainly affected by two key elements, users and their service situations. Firstly, the latent semantic relations among users, their criteria preferences and service situations are established with latent classes in this model. In order to describe multifaceted characteristics of users, service situations and users' criteria preferences, all of them are allowed to simultaneously belong to multiple latent classes with different probabilities. Afterwards, the expectation maximization algorithm and the consistent training data obtained by analytic hierarchy process are used to estimate the parameters of the latent semantic probabilistic model which contains latent variables. Finally, the trained model is employed to predict users' criteria preferences under specific service situations if users are unwilling to provide their criteria preferences due to lack of domain knowledge. The main advantage of the proposed latent semantic probabilistic model over the standard memory-based collaborative filtering and the collaborative filtering improved by clustering is an explicit and compact model representation. And the experimental results show that the algorithm based on the latent semantic probabilistic model can get higher prediction accuracy than both the standard and the improved collaborative filtering algorithms and can also alleviate the impact of data sparsity.
关键词	Web服务推荐个性化隐语义概率模型指标偏好期望极大算法 Web Service Recommendation Personalization Latent Semantic Probabilistic Model Criteria Preference Expectation Maximization Algorithm
部门归属	中国科学院软件研究所天基综合信息系统重点实验室北京 100190;中国科学院大学北京 100049 中国科学院软件研究所天基综合信息系统重点实验室北京 100190
语种	中文
CSCD记录号	CSCD:5222025
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16710
专题	中国科学院软件研究所
通讯作者	Hu, Yan
推荐引用方式 GB/T 7714	胡堰,彭启民,胡晓惠,等. 一种基于隐语义概率模型的个性化Web服务推荐方法[J]. 计算机研究与发展,2014,51(8):1781-1793.
APA	胡堰,彭启民,胡晓惠,&Hu, Yan.(2014).一种基于隐语义概率模型的个性化Web服务推荐方法.计算机研究与发展,51(8),1781-1793.
MLA	胡堰,et al."一种基于隐语义概率模型的个性化Web服务推荐方法".计算机研究与发展 51.8(2014):1781-1793.