Research On Personalized Recommendation Algorithm For Insurance Products

In the current era of explosive growth in the amount of information,the recording of users' online behaviors and the mining of their intents and preferences can provide strong support for the operation and marketing of enterprises.The insurance industry has gradually become a feature of the user's insurance behavior.Research has attracted more attention from the industry.The business form of the insurance industry has produced a large amount of high-quality and valuable data.It has great value for excavation and use.On the one hand,insurance users can obtain explosive growth of information,and on the other hand,users choose information.The limited capabilities of the company have made it difficult for insurance companies and users to solve the problem of information overload that insurance users face through personalized recommendation technology.Based on this,the thesis carried out the following three aspects of research:(1)Analyze the data on the purchase of insurance products by users,count the number of purchases made by consumers,the sales of insurance products,and the activity of users.From the perspective of human dynamics,tap the behavioral characteristics of the purchase behavior of insurance users,just like other human behaviors.It is mainly characterized by the "Strong burstiness and weak memory."(2)Research on the recommendation of insurance products using commonly used collaborative filtering and recommendation algorithms based on association rules.Two combination strategies are proposed based on the two recommendation algorithms.Based on the traditional content recommendation algorithm,a content recommendation algorithm based on diversity metrics is proposed for the long tail feature of the purchase behavior of insurance users.Also,according to the use scenario of insurance users,a personalized recommendation technology based on the user's real-time behavior is proposed.(3)Participate in the design and implementation of a personalized recommendation system based on insurance products,and apply the personalized recommendation algorithm to the personalized recommendation system of insurance products,and introduce in detail the framework design of personalized insurance product marketing modules.As well as processing off-line analysis and on-line recommendation process,the actual recommendation effect of the personalized recommendation algorithm of insurance products is evaluated from both the algorithm and marketing perspectives.In the current insurance industry's demand for personalized recommendations,the thesis analyzes the behavior patterns of insurance product users through data analysis,studies the personalized recommendation methods applicable to insurance products,and analyzes and evaluates various personalized recommendation algorithms.Selecting suitable algorithms for user scenarios to design a complete insurance product personalized recommendation system.