Research On Personalized Recommendation Of E-commerce Based On LBS

With the information explosion and the arrival of the Web3.0 era,personalized recommendation is more and more widely applied,but the missing of position data leads to the failure of providing information that meet the individual needs of users,and with the rapid development of mobile Internet,the increasing popularity of mobile terminals,the spatial information technology is becoming more complete,the mobility,practicality,time-unlimited and other characteristics of the LBS make it possible to solve this problem : personalized recommendation based on LBS beyond every restriction of time and space,quick response,instant delivery of information are the key to promoting user experience of the personalized e-commerce recommendation system. Therefore,this paper approaches e-commerce personalized recommendation based on LBS.The innovations of this paper include:(1)Integrating user location situation information into e-commerce personalized recommendationBy in-depth literature analysis of the research status of personalized recommendation based on LBS,as most of the studies were based on geographical location information,this paper integrating the location context information of the user into the e-commerce personalized recommendation to make the real needs of individual users be more truly reflected,which is a new attempt to study e-commerce personalized recommendation based on LBS.(2) A new e-commerce personalized recommendation algorithm based on LBS is proposed.As most of the studies personalized recommendation location situation based on LBS,this paper propose the arrival time,the conception of user state to specific user location scenarios(decomposing into three kinds of context information of users arriving time, weather and user status then integrating into personalized recommendation),respectively,analysis user information needs of three kinds of situations of users arriving time, weather and user status, and formulate the corresponding personalized recommendation algorithm.(3) design and implement a prototype system of e-commerce personalized recommendation based on LBSUsing the data acquisition module to collecting raw data and context data,design personalized e-commerce recommendation algorithm based on rules according to three scenarios of the user arrival time,weather and user state,finally by the adaptive system without explicit user input preference,make full use of user context information to provide more accurate and personalized recommendation.This paper is divided into six chapters,as follows:The first chapter introduces the background and significance of this paper,analyzes the research status of personalized recommendation and personalized recommendation based on LBS,and describes the research programme and organizational structure.The second chapter introduces the LBS, e-commerce personalized recommendation,a detailed analysis of the existing key technologies and development and the applications of personalized based on LBS,lay the foundation for the design and implement of a e-commerce personalized recommendation based on LBS prototype system.The third chapter according to the related theory and technology,analyze and design basic principles to be followed when developing the e-commerce personalized recommendtion based on LBS system,propose design idea,system structure,function modules, operation mechanism, technical solutions of e-commerce personalized recommendation based on LBS system.The fourth chapter describes the technology selection and deployment environment of Android platform of the e-commerce personalized recommendation system based on LBS,presents the construction of e-commerce personalized recommendation system based on LBS from four aspects of the user interest modeling,recommendation mechanism implementation, information resources organization, recommendation system effect display.The fifth chapter,from the angle of business management,introduces practical application and future trend of e-commerce personalized recommendation based on LBS according to four aspects of comparing existing application situation,application mode,application value,and application prospect.The sixth chapter summarizes the work of the paper,and looks forward to the follow-up research work.