基于模糊概念格的领先用户个性化知识推荐研究

Research on personalized knowledge recommendation for leading users based on fuzzy concept lattice

摘要:

针对创新社区日益增长的海量信息阻碍了用户对知识进行有效获取和创造的现状,将模糊形式概念分析（FFCA）理论应用于创新社区领先用户的个性化知识推荐研究。首先识别出创新社区领先用户并对其发帖内容进行文本挖掘得到用户——知识模糊形式背景,然后构建带有相似度的模糊概念格对用户偏好进行建模,最后基于模糊概念格和协同过滤的推荐算法为领先用户提供个性化知识推荐有序列表。以手机用户创新社区为例,验证了基于FFCA的领先用户个性化知识推荐方法的可行性,有助于满足用户个性化知识需求,促进用户更好地参与社区知识创新。

Abstract:

The increasing mass of information in innovative communities is hindering users effective acquisition and creation of community knowledge, so the Fuzzy Formal Concept Analysis (FFCA) theory is applied to the research of personalized knowledge recommendation for leading users in an innovative community in this paper. First of all, the user-knowledge fuzzy formal context is obtained by identifying the leading users of the innovative community and text mining the content of their posts. Then, a fuzzy concept lattice with similarities is constructed to model user preference. Finally, a recommendation algorithm based on fuzzy concept lattice and collaborative filtering is proposed to provide personalized knowledge recommendation for leading users. Taking the innovative community of mobile phone users as an example, we use the algorithm proposed in this paper to get an ordered list of personalized knowledge recommendation for leading users, so as to improve their enthusiasm to participate in community knowledge innovation.