基于数据挖掘的电视产品营销推荐

北京理工大学珠海学院2020届本科生毕业论文TV product marketing recommendation based on data miningAbstractIn today's big data era,while the Internet is developing rapidly,the TV media in-dustry has also ushered in its own development opportunities.With the continuousgeneration of massive user viewing information data every day,users are also facinga large number of TV product selection issues.In this regard,this article uses datamining to achieve personalized recommendation of TV products for users.This article uses the data of the 2018 "Teddy Cup"B competition to do the mainanalysis and research on the user's viewing information data and TV product in-formation data.First,perform data preprocessing,including removing extraneousvariables,cleaning additional information,and merging based on keyword data.Then,count the frequency and duration of users'on-demand,and do descriptivestatistics and data visualization.Then,supplement the types and ratings of TVmovies through crawlers,and classify TV products according to their type label-s.Establish user viewing preference models,analyze user interest dimensions,andintroduce interest levels.Based on the user's collaborative filtering recommendation algorithm,the user-movie matrix,user-preference type matrix,user cosine similarity matrix are estab-lished,the topN similar users and TV product recommendation list are obtained,the specific precise marketing plan is given,the recommendation index is calculatedand recommended.In order to further expand the scope of product marketing,K-means clustering is used to package into the user group,and then combined withthe classified TV product list for batch recommendation.Keywords:Collaborative filtering cosine similarity user viewing preference mod-el Recommendation Index k-means clustering