A Research On Intelligent Recommendation System Of Mobile Internet Digital Reading Website

The integration of the mobile communication technology and the Internet technology leads the human into a new era of mobile Internet. The mobile digital reading attracted people recently as a newly emerging industry. Online recommendation are important means to propel the consumption of the digital content goods. Online recommendation is an effective tools to help consumers solve the information overload, filter and find the target products and improve the quality of decisions, but scholars remain suspicious of its effect.In addition, there is a process of experience and continuous consumption, thus the influence and effect as mentioned above will become more complicated. In this case, this paper studies into the integration effect of online recommendation to the experience and consumption of digital content products, explores the influential factors and mechanism, and aims to gives better guidance for the management of the online recommendation.the essay has done some innovation on data collection and research methods. Using big data method, we collect a large sample of deep panel data on the Mobile Reading platform, like user's visiting route, detail pages' quantity of visiting users, sta ying rate in every chapter of a book and so on. With these we construct the big-data foundation which surpasses previous researches. In the meantime, this essay does multiple regression analysis based mainly on panel data, and controls the inner difference of books using fixed effects method. In the research of continuous experience consumption stage, the essay constructs a creative method of segmented multiple regression.