Study On The Influnce Of Personalized Recommendation Of Online Platform On Customers' Purchase Intention

Nowadays,e-commerce is developing rapidly,online shopping is favored by more and more consumers.Online shopping platform and various product information are also exploding.This allows consumers to enjoy the convenience brought by the online shopping platform,but also fall into the difficult situation of product selection.In order to help consumers screen information and improve purchase efficiency,personalized recommendation technology came into being.Major online shopping platforms apply recommendation technology to collect consumers' personal information and behavior data,and provide personalized recommendation services for consumers by analyzing and mining the data.And this kind of personalized recommendation service can be regarded as a marketing tool,so how to make this marketing tool play more potential and make website push services more attractive to customers has become a key issue in front of many online shopping platforms.This paper reviews the previous literature,summarizing the measurement dimensions of personalized recommendation of online shopping platform into information arrangement,recommendation strength,recommendation methods,interactivity and security,taking perceived trust as the mediating variable and purchase intention as the dependent variable,a study model was constructed based on the S-O-R model to explore the impact of personalized recommendation of online shopping platforms on perceived trust,and then the impact on consumers' purchase intention.Research shows:each measurement dimension of personalized recommendation of online platform positively affects consumers' perceived trust;perceived trust positively affects purchase intentions;perceived trust plays a partial mediation role between personalized recommendation of online shopping platforms and consumers' purchase intention.On this basis,it is proposed to further optimize personalized recommendation services from five aspects: standardizing information layout,reasonable control of recommendation times,diversified recommendation methods,establishing effective interaction methods,and strengthening supervision of online platforms.