摘要
日益丰富的门户个性化服务为兴趣建模提出了在描述以及扩展方面更高的要求.提出一种基于潜在兴趣语义描述的门户个性化兴趣模型(PIM-LISD,Personalization Inter-estModel based on Latent Interest Semantic Description),遵循有限混合模型理论,融入了隐式获取的门户个性化兴趣语义以及潜在兴趣语义关联描述.通过合理选取不同先验分布来适配潜在兴趣语义的后验分布,完善了模型的可解释性和自适应能力,并在建模过程中利用优化的期望最大化(TEM,Tempered Expectation Maxim ization)算法进行参数估计.实验表明该方法不仅有效节省建模开销,而且能够提升预测精度,从而验证了其正确性和有效性.
There is a great potential for interest modeling on description and extension as the personalization services enrich the portal. A novel personalization interest model based on latent interest semantic description （PIM-LISD） was proposed. As a finite mixture, it was represented with personalized interests elicited implicitly on portal and latent interest relating semantic descriptions. The initialization method was to fit the posterior probabilities along with the different reasonable prior predictions, so the interpretative and adaptive capacity of the model could be perfect. During the building procedure, an improved tempered expectation maximization （TEM） was used for variational expectation maximization estimation. The experiments indicate that the proposed modeling method can not only avoid the complication of potential expensive modeling-building stage effectively, but also increase the prediction precision, therefore proving its validity and feasibility.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2008年第2期148-152,182,共6页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家高技术研究发展计划(863计划)滚动课题资助项目(2002AA113070)
关键词
兴趣建模
潜在语义分析
有限混合模型
先验分布
interest modeling
latent semantic analysis （LSA）
finite mixture model
prior prediction
