一种基于隐语义概率模型的个性化Web服务推荐方法

A Personalized Web Service Recommendation Method Based on Latent Semantic Probabilistic Model

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.

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.