E-commerce Personalized Recommendation System

With the popularity of e-commerce and related technologies improving, more and more people accept the consumption patterns of net purchases, but the contradiction between the relative stability of personal needs and the numerous and complex goods becomes increasingly sharp, and how to resolve this problem become a hot spots in research. The personalized recommender systems are regarded as the study object. The focus is the ways to better use of preference that the user has consciously or unconsciously reflect, for the personalized recommendation service prototype system.The advantages and disadvantages of the explicit and implicit user preferences access are analyzed and a mixed-mode preference access pattern is proposed. Users' browsing behavior is collected by the client browser and is converted to the ratings by transformation rules, which can make up for sparse explicit data. The mixed-model combines the implicit and explicit ratings to express user profile. For such extremely frequent changes in user interest, a linear-attenuation based user profile is proposed. The registered information and browsing behaviors are used to set up the initial user profile. Because of the frequent change of user interests, use profile is represented by a chain vector space model. User ratings decrease at a fixed time interval t until the rating has been eliminated to zero. User profile updates rating by a new record of visits and explicit rating, and then continues to be involved in the attenuation process. These constitute a model of user profile in the main update process.Collaborative filtering based recommendation prototype system test the similarity algorithms, the neighbor set size and collaborative filtering algorithms, which verify the feasibility of the system. The special script embedded browser test the user residence time, mouse clicks and page scrolling time that may reflect user preferences. The test results show that the residence time and scroll the page of time associated with the user preference closer and mouse clicks don't show an obvious correlation.