Research And Implementation Of Personalized Recommendation System For Insurance Product

With the advancement of science and technology and the maturity of information technology,the development of the insurance industry has also been integrated into the wave of Internet development,and online sales of insurance products have gradually become one of the main forms of insurance sales.With the surge in the number of insurance products sold online,there is often a problem that policyholders have no way to start in the face of a large number of insurance products,and the insurance products recommended by insurance salespeople for policyholders have strong subjective factors,so they cannot accurately recommend products for policyholders.At present,the development of the recommendation system has gradually matured,but the traditional recommendation system often has the problem of cold start when there are more new users,the problem of poor effectiveness of recommendation due to more calculation data,and the problem of poor recommendation effect caused by the use of a single recommendation algorithm.Therefore,in order to accurately and quickly recommend products that meet the preferences of policyholders,this project designed and implemented an insurance recommendation system.This article mainly does the following work:(1)Aiming at the cold start problem of traditional recommendation algorithms,a collaborative filtering hybrid recommendation algorithm integrating document topic algorithm(LDA)and alternating least squares algorithm(ALS)is proposed in the offline recommendation module,which effectively alleviates the cold start problem in the traditional recommendation system;Aiming at the effectiveness of traditional recommendation algorithm recommendation,a collaborative filtering algorithm(EOCF)based on expert trust is proposed in the real-time recommendation module,which fully considers the similarity of users and products,ensures the effectiveness of the recommendation system and effectively improves the accuracy of recommendation results.(2)The feasibility study of this topic was carried out in the process of system design and construction,which proved that the topic had high feasibility,and then the overall needs of users were analyzed in depth and the main tendencies of user needs were classified,on this basis,the overall function of the system was divided into several different functional modules,and finally the overall architecture design was carried out according to the characteristics of each functional module.(3)According to the system design,the specific construction of the insurance recommendation system was carried out,and a reliable and efficient insurance product recommendation system was built by using the popular big data platform framework Spark combined with Kafka,ElasticSearch,Redis and other commonly used big data processing tools.Finally,a complete system test was carried out,mainly including concurrent test,functional test,etc.,which effectively ensured the reliability of the insurance recommendation system.