Application And Research Of Collaborative Filtering On Recommendation Systems For E-Commerce

Recently, Recommendation Systems for E-Commerce have obtained prevalent success. Collaborative filtering is one of the widely recommendation technologies of individuation._Whereas, there are some problems in Recommendation Systems for E-Commerce, such as sparse data of users' rating, bad expansibility and lack of multiformity, can not cover all the interests of users. Meanwhile, quantity of valid information on net and sorts of zoomed commodity bring with austere challenge.This thesis has researched recommendation system of individuation and main recommendation technology of individuation, especially collaborative filtering technology which includes collaborative filtering based on users and collaborative filtering based on items. The work and innovation of this thesis has four parts as follows:Firstly, in computing user's similarity phase, this thesis puts forward a mended similarity compute method based on users' drifting interests which considering the impact of user's rating time. Sequentially, it obtains the most valid nearest neighbor of active user.Secondly, reaching valid neighbor is very important. This thesis adopts users' feature to cluster users. Then through the mended method of user's similarity from cluster which active user in, it can reach the nearest neighbor.Thirdly, in forecast phase, the thesis adopts the nearest neighbor who can attain the lowest MAE using mended algorithm. And this new method is validated that it can more enhance accurate of commendation than collaborative filtering based on user.Fourthly, the thesis adopts several models recommendation. At the same time, it adopts mode method to solve cold start (new item or new user) so that enhancing the quality of recommendation system.Subsequently, this thesis takes an emulate experiment, analyzes results of experiment, compares the mended recommendation algorithm and tradition recommendation algorithm and proves mended recommendation algorithm is more precise than tradition recommendation algorithm.At last, this thesis designs and implements a simple recommendation system of film which uses the mended algorithm.