The impact of personalized recommendation on purchase intention under the background of big data

In order to address the problem of information overload, numerous solutions have been proposed, among which recommender systems stand out as one of the most effective. By reviewing and organizing existing literature both domestically and internationally, this paper evaluated personalized recommendations on the Internet across six dimensions: information layout, recommendation way, recommendation strength, recommendation precision, recommendation timeliness, and recommendation interactivity. Simultaneously, it considered the customer's mind-flow experience and perceived trust as mediating variables, and consumer purchasing intention as the dependent variable of the study. Furthermore, based on relevant theories, this paper constructed a corresponding research model to explore how personalized recommendations influence customers' intention to purchase by affecting their mind-flow experience and perceived trust through this model.