基于热扩散影响力传播的社交网络个性化推荐算法

有针对性地为用户提供推荐,提高互联网信息利用率是个性化推荐系统的主要目标.文中基于热扩散传播概率模型,结合用户在社交网络中隐含的跟随关系,提出基于热扩散影响力传播的社交网络个性化推荐算法.首先,算法将现实生活中人与人的朋友关系转化为购物网络中用户与用户的跟随关系,构建异构信息网络图,计算用户之间的复合相似度.然后,利用基于热扩散概率模型模拟社会网络中影响力的传播过程,计算社交网络中用户的跟随概率分数并精确排序,筛选与目标用户相似的邻近用户.最后,根据目标邻近用户对各个产品的评分,将评分较高、具有潜在兴趣的产品推荐给目标用户,实现个性化的用户推荐.在公开数据集上与现有的个性化推荐算法进行对比,实验表明,文中算法具有较好的精确度和多样化的推荐效果.

Heat Diffusion Influence Propagation Based Personalized Recommendation Algorithm for Social Network

The primary objective of a personalized recommendation system is to provide pertinent recommendations for users and improve internet information utilization. A social network personalized recommendation algorithm based on heat diffusion influence propagation(HDIP) is proposed in this paper combining HDIP with the hidden follow-up relationship in the social network of users. Firstly, in the HDIP algorithm, the friendship in real life is transformed into follow-up relationship between customers in shopping network. Heterogeneous information network graphs are constructed and the composite similarities between users are calculated. Secondly, the influence propagation process in social networks based on the heat diffusion model is simulated. Probability scores of users in the social network are calculated and accurately sorted to select neighboring users similar to the target users. Finally, the products of potential interest are recommended to the target users according to the ranking. Thus, the personalized recommendation is implemented. The public dataset is utilized for the comparison between HDIP and conventional recommendation algorithms. The experimental results show that HDIP produces a relatively high accuracy and various recommendation effects.