The Research Of Personalized Information Recommendation Service And Application In Electronic Business

With the rapid development of E-business based on Internet,the intensity and density of information are unheard-of, the magnitudes of users and commodities grow rapidly by progression,so personalized information recommendation service becomes more and more important. On the aspect of theory research and factual application,E- business personalized recommender systems have developed rapidly. Recommendation approaches are the core part of recommender system,so which approach is adopted is crucial to the success of recommending quality as well as efficiency. Collaborative filtering algorithm is the prevalent recommendation method at present.However,because the proportion of user rating data is few, the sparsity of data influences the quality and the real-time requirement of recommendation.A collaborative filtering recommendation method based on items and customers clustering was proposed in this paper to solve these problems: This paper studied deeply E-commerce recommendation system, analysed the collaborative filtering algorithm and challenges which collaborative filtering recommendation approach suffered from,then we presented an improved collaborative filtering algorithm based on clustering. The followings are the main steps of improved algorithm:Firstly,to compute offline items similarity based on the Users-Items rating database,and to save results in database;Secondly,for every clustered sets ,to compute users similarity and find out the user's neighbors sets.Finally,to predict every item's rating according to the nearest neighbors and to generate recommendation to target users.We tested the improved algorithm through programming,the experiment result proved that this algorithm is feasible and effective. Compare to traditional collaborative filtering algorithm,this algorithm can overcome the sparsity of user's rating information and generate better recommendation results. The last chapter of paper showed the whole frame of personalized recommendation system,including the description and flow chart of personalized recommender model.