摘要：

现有兴趣模型难以直接描述综合集成研讨环境(CWME)中的专家兴趣。为此，提出一种面向CWME的专家兴趣建模方法。该方法采用非负矩阵分解技术自动生成研讨兴趣话题，通过分析专家发言特征词与兴趣话题的关系，生成专家兴趣信息，整合专家兴趣信息后得到层次化组织的专家兴趣模型。实验结果表明，应用该模型能够较好地实现研讨领域预测和针对具体专家的个性化信息推荐。

关键词: 综合集成研讨环境, 兴趣模型, 非负矩阵分解, 特征词, 层次模型, 个性化推荐

Abstract:

Traditional user models can not describe the interest of the experts in Cyberspace for Workshop of Metasynthetic Engineering(CWME) well. To meet this challenge, this paper proposes a new expert interest modeling method. This method uses Non-negative Matrix Factorization(NMF) to generate the elaborate topics, then works out the interest segments of an expert step by step through analyzing the relationship between the expert's speech and topics. The model is finally built by integrating all the interest segments of the expert. Experimental results show that the proposed model can predict discussion domain accurately and is helpful to recommend personalized information for participants in CWME.

Key words: Cyberspace for Workshop of Metasynthetic Engineering(CWME), interest model, Non-negative Matrix Factorization(NMF), feature word, hierarchical model, personalized recommendation

中图分类号: 

• TP391