Social Network Recommendation Algorithm Based on ICIP

The recent years has witnessed rapid development of social network platforms. To enable timely and effective selection of information that is valuable and raises interests, a variety of personalized recommendation algorithms have been put into practice. The improved clustering based on the Isolation Point (ICIP) algorithm is presented based on clustering. In order to overcome the shortcomings of the traditional clustering, the process of Isolation Point has been included in the ICIP algorithm. In this paper, the ICIP algorithm is used for topic extraction of WeChat articles. According to the characteristics of the social network platform, the data noise reduction and modeling is adopted first and then text classification is achieved based on the similarity. The ICIP is applied to remove isolated points, improve clustering accuracy and reduce noise. Compared with other clustering algorithms, the ICIP algorithm has higher accuracy and efficiency.