Abstract
In order to improve the intelligent search capabilities of Internet financial customers, this paper proposes a search algorithm for Internet financial data. The proposed algorithm calculates the customers corresponding to the two selected financial platforms based on the candidate customer set selected from the seed dataset and combined with the restored social relationship. Moreover, it also calculates the similarity of each field between the pairs. Furthermore, this article proposes an entity customer classification model based on logistic regression. Through the SNC model, threshold propagation, and random propagation, the model is transformed into an algorithm that identifies the associated customers, eliminates redundant customers, and realizes associated user identification. Experimental results verify that pruning increases the accuracy of identifying related customers by 8.44%. The average sampling accuracy of the entire customer association model is 79%, the lowest accuracy is 40%, and the highest is 1. From the sampling results, the overall recognition effect of the model reaches the expected goal.
Highlights
In recent years, IT has benefited from the increasing development of the Internet and the in-depth development of financial disarmed. e Internet finance has rapidly and evolved into a global financial phenomenon and become one of the most popular financial topics
The term “Internet finance” is used for the funding achieved by traditional financial institutions and Internet companies through using the Internet and information communication technologies. e sellers on the platform characterize the features of the Internet financial platform. at means, the higher number of buyers attract the sellers to that very platform
Logic Regression. e candidate customer set according to the seed dataset is used, combined with the reduced social relationship; first, the similarity of the two financial platforms corresponding to each field between the two financial platforms is calculated, combining the similarity of the field into similarity feature vectors as follows:
Summary
IT has benefited from the increasing development of the Internet and the in-depth development of financial disarmed. e Internet finance has rapidly and evolved into a global financial phenomenon and become one of the most popular financial topics. The Internet finance has played a role of traditional financial institution to replace the active role in promoting direct financing, improving the efficiency of financial services and resident financial services, and fully excavating and catering the market demand. It is called “squid effect” and contributing positive energy [1]. Erefore, in this paper, the customer association confirmation selects the logistic regression model for classification. E results belong to positive examples (represented by 1) and negative examples (represented by 0), and the conditional probability of binomial logistic regression is Feature vector library. Simrank [11], originally proposed by the MIT Lab Glen Jeh and Jennifer Widom in 2002, is a model of the topology information that uses the map to measure the similarity of the two objects. e core idea is if the two objects are referenced (in the social network is expressed as a similar neighbor), the two objects are similar
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