Application of clustering algorithm in social network user scenario prediction

Abstract

In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better.

A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory

“…As for the requirement analysis methods, two types of methods are mainly applied, including traditional research methods, such as the KJ method, user experience journey map, empathy map, and data analysis methods, such as machine learning algorithm and cluster analysis (Kim and Jun, 2015;Yu et al, 2020). For example, clustering algorithms are used to analyze web log data (Tang et al, 2020;Wen et al, 2020) (such as purchase records and online behaviors) and word frequency statistics are used to extract keywords in user comment texts (Zhao et al, 2019;Zhu et al, 2020). Among them, Li N. L. et al (2020) proposed a model to identify key demands and used text mining and clustering algorithm to analyze online user comments.…”