基于用户偏好特征挖掘的个性化推荐算法

为了提升社交网络个性化推荐能力,结合用户行为分布进行个性化推荐设计,文中提出基于用户行为特征挖掘的个性化推荐算法,构建社交网络的用户行为信息特征挖掘模型,采用显著数据分块检测方法对社交网络用户特征的行为信息进行融合处理,提取反映用户偏好的语义信息特征量。从情感、关键词和结构等方面根据用户行为特征组,结合模糊信息感知方法进行社交网络个性化推荐过程中的信息融合处理,在关联规则约束控制下,构建社交网络用户偏好特征的混合推荐模型,实现用户偏好特征挖掘,根据语义分布和用户的行为偏好实现社交网络的个性化信息推荐。仿真结果表明,采用所提方法进行社交网络个性化推荐的特征分辨能力较好,对用户行为特征的准确识别能力较强,提高了社交网络推荐输出的准确性。

Personalized Recommendation Algorithm Based on User Preference Feature Mining

For the purpose of personalized recommendation ability of social network,this paper proposed a personalized recommendation algorithm based on user behavior feature mining according to the distribution of user behavior.The user behavior information feature mining model of social network is constructed,the big data fusion scheduling method is used to fuse the behavior information of social network user characteristics,and the semantic information features that reflect the user preference are extracted.According to the user’s behavior feature groups from the aspects of emotion,keywords and structure,combined with the fuzzy information perception method,the information scheduling in the process of personalized recommendation of social network is carried out.Under the control of association rules constraints,a hybrid recommendation model of user preference features is constructed to realize user preference feature mining,and personalized information recommendation of social networks is realized according to semantic distribution and user behavior preference.The simulation results show that the poposed method has good feature resolution ability and accurate recognition ability to user behavior features,which improves the confidence level of social network recommendation output.