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.
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2019, Vol. 32 
