Research On The Application Of Hybrid Recommendation System In E-commerce

At present,the advent of a highly informatized era in human society is inseparable from the rapid development of various Internet technologies.People often rely on the Internet to obtain various information.As the development of the Internet world becomes more and more abundant,the resources on the network increase exponentially,which is accompanied by the problem of Information Overload.In the current context,the recommendation system has become an effective way to solve this problem.In this era of e-commerce,which has gradually evolved into a swift and fierce era,it has become increasingly difficult and complicated to obtain useful information for people from massive amounts of data.Discovering useful information from massive user behavior data,product data,etc.and using this information to obtain users in a shorter time and more accurately,and helping merchants to accurately locate potential users of products has become a hot spot in various e-commerce platforms.problem.In the Internet era,the recommendation system is the core technology of all user-oriented products.It recommends a small collection of products that users are interested in.The recommendation system can not only solve the problem of information overload,but also bring more benefits for enterprise operations.This article conducts relevant research on the characteristics of e-commerce systems and related technical principles of current e-commerce recommendation systems.It introduces the basic idea of the algorithm for content-based recommendations,collaborative filtering-based recommendations,and collaborative filtering-based recommendations commonly used in personalized recommendation systems.,And its own advantages and disadvantages in application scenarios;and then leads to three hybrid recommendation modes based on multiple algorithms,namely overall,parallel,and pipeline.Different hybrid modes are conducive to making different algorithms.Complement each other and make use of its strengths to make up for its deficiencies,which will make the recommendation results of the recommendation system more accurate,diverse and novel.It also discusses the problems facing the current recommendation system and gives common solutions to alleviate their problems.Subsequently,the key technologies commonly used in the recommendation system are introduced in detail.The basic ideas of the FLM and TF-IDF algorithms are introduced in detail,and a hybrid collaborative filtering recommendation model that combines recommendation based on user attributes and FLM is proposed to alleviate the problem of cold start and data sparsity;it incorporates project popularity penalty factors and user activity TF-IDF algorithm with degree of penalty factor and collaborative filtering hybrid recommendation model improve the diversity of recommendation results and alleviate the long-tail problem of commodities.And experiment and evaluate its effectiveness on MovieLens dataset.At the end,based on the above work,combined with the actual e-commerce business scenario,the construction of an e-commerce system based on the hybrid recommendation algorithm,which integrates a variety of recommendation algorithms and supports Spark technology.