Research On Collaborative Filtering Recommendation In Electronic Commerce Based On Fuzzy Clustering

With the internet occupy more and more important position in people's daily life.Electronic commerce system also begin to expand rapidly along with it,Therefore the recommendation system can also apply in the electronic commerce system widespread.The recommendation system is the system that it can use the users'purchase history,according to their former customs recommend information or the commodity to the users which suit to them.At the present,the recommendation system has been utilized in every profession, such as Amazon,CDNOW,eBay,and Dang dang on-line bookstore etc,have used various recommend -ation system in the different degree.As the most successful personalized recommendation technology of recommendation system in present,collaborative filtering recommendation is obtaining more and more attention of researchers.This article basied on researching collaborative filtering recommendation algorithm analyzes the question which still existed in the present research,its emphasis is analyse the cold start question,the sparse question,the new project question,the new user question,and so on user multi-interests question existing in the system.Then,the article proposes an improvem -ent algorithm which based on fuzzy clustering to research collaborative foltering recommendation in electronic commerce. The article's concrete work contains the following several aspects:First, The article has analyzed the domestic and foreign electronic commerce recommendat ion system's present research situation and future development direction.It also has introduced the electronic commerce recommendation system simply which based on the content and based on the connection rule recommendation algorithm;It elaborated collaborative filtering recommendation algorithm in detail,introduced its basic steps,analyzed its merit,at the same time,pointed out some realistic questions which exists in algorithm. Second, the article specificed to the collaborative foltering recommendation algorithm's shortcoming,and unified the FCM cluster technology,it came up with an algorithm which is collaborative filtering recommendation in electronic commerce based on fuzzy clustering.Its basic mentality is: First, the article bring FCM clustering technology into user-project grading model,according to the questions which exists in the collaborative filtering recommendation algorithm,made the improvement to FCM.Then, the article carried on the cluster with improvement FCM to the data set,that it can obtain the testing user's neighbor users,according to the neighbor users calculated the testing user's predicted value,at last,it gave testing user the recom-mendation.Third,using the MovieLens database,the artlcle carried on experiment simulation which and test to improvement algorithm proposed,compared with the based on k-means algorithm and traditional collaborative filtering recommendation algorithm,the experiment demonstrated that the algorithm has the high recommendation quality compared to the traditional algorithm.