基于潜在因子模型的跨领域信息推荐算法  

Cross-Domain Recommendation Algorithm Based on Latent Factor Model

作　　者：高升[1] 任思婷 郭军[1] 

机构地区：[1]北京邮电大学,北京100876

出　　处：《电信科学》2015年第7期75-79,85,共6页Telecommunications Science

基　　金：国家自然科学基金资助项目(No.61300080;No.61273217);教育部-中国移动科研基金资助项目(No.MCM20130310)~~

摘　　要：互联网环境下,不同领域中多源异构信息对象的交互融合使用户面临大数据环境下的信息选择困境,传统的信息推荐算法已很难适应跨领域的信息推荐服务。综合分析了不同领域内用户对信息对象的评价数据,基于潜在因子模型抽取了不同领域中某一用户聚类集合对某一信息对象聚类集合评分模式的跨领域共性特征和单领域个性特征,进而通过传递、共享跨域共性特征信息的方式缓解了目标领域的数据稀疏性问题,提高了跨域信息推荐的准确度。In the internet environment, the combining of multi-source heterogeneous information objects in different areas makes users face information selection dilemma problem in big data environment. It has been very difficulty for traditional information recommendation algorithms to adapt to the interdisciplinary information recommendation service. The evaluation model from a user clustering set to an information object clustering set has common characteristics of cross-domain and personality characteristics of single domain. By analyzing the evaluation data from users to information objects in different areas, these characteristics were extracted based on latent factor model. Then by transmitting and sharing the common characteristics of cross-domain, the data sparseness problem of target field was alleviated, which could improve the accuracy of cross-domain information recommendation.

关 键 词：跨领域信息推荐 潜在因子模型 用户评价模式 

分 类 号：TP393.4[自动化与计算机技术—计算机应用技术]