摘要
为准确把握用户兴趣,提高用户体验,实现内容的精准推广和用户的个性化服务,建立移动社交网络用户兴趣模型。挖掘用户的移动社交网络行为及内容,抽取用户的兴趣特征项,在统计兴趣项词频的基础上借鉴改进词频—逆文档频率(term frequency—inverse document frequency)算法以计算用户兴趣度权重,得到用户兴趣的向量空间表述模型。试验表明该方案在用户兴趣识别与排序上准确率较好。
A users’interest model for mobile social network is built in order to grasp exactly users’interest,to improve their experience and to realize a precise information push service and individualized service. The paper examines users’mobile social network behaviors and extracts their interest featuring items. Then it calculates interest word frequency and gains users’interest weight by referring to and improving the algorithm of term frequency-inverse document frequency (IF-IDF algorithm),based on which a vector space representation model fo rusers’interest is built. Experiment results indicate that this model holds accuracy in users’interest recognition and sequencing.
作者
杨中秋
季莉
YANG Zhongqiu;JI Li(Jiangsu College of Engineering and Technology,Nantong 226007,China)
出处
《江苏工程职业技术学院学报》
2016年第2期4-7,共4页
Journal of Jiangsu College of Engineering and Technology
基金
江苏省高等学校大学生创新创业训练计划项目(编号201510958014Y)
