Research And Design For Personalized Recommendation System Based On Mobile Reading

With the rapid development of the Internet, the information content of data is moreand more large-scale in today’s society. In the context of the era of big data,the form thatpeople capture information has undergone a huge change. A more popular topicispresented in the IT industry, which ishow to findthe data for personal taste in the floodof information data. Personalized recommendation becomes another explosive growthof the Internet industry point.This thesis introduces an improved recommended method which makespersonalized recommendation more humane. Mobile phone reading such an electronicreader products, for example, from several angles of building， organization， using ofdata to elaborate the operating mechanism of the personalized recommendation system.Propose a set of analytic method to mining the data of users’ interest and the behaviorfeature based on the telecommunications users’url visit data.Furthermorecombined withindicators of the characteristics of human behavior in human dynamics, webuild themobile phone users’interest map data.In this thesis, we use the personalizedrecommendation algorithm results, behavior of the individual characteristics and users’interest map data to recommend information.Construct not only the contentpersonalization, and service personalized recommendations. Meet the marketing needsof operators, while improving the effect of personalized recommendation, personalizedrecommendation in the users’ view point is more humane.Thethesisis positive significanceto the construction of the personalizedrecommendation system, and with reference value for Characteristics of user behaviorand interest in data mining.