Reseach On Online Bookstore Recommendation Algorithm Based On Content Clustering

With the rapid development of science and technology,Internet information also presents the increasing trend of explosive,the vast amounts of data,thus increased the pressure of information overload.Due to a large number of data in the rapid and effective auxiliary user for the required information,Internet information become great important in the academia and industry.Facing this situation,filtering information recommendation algorithm is widely used in various range.Recommended calculation method is seen as one of the core tools,more and more attention by experts and scholars.Collaborative filtering recommendation is seen as a recommended method.However,the recommended precision is not high enough,cold start and poor scalability affect the function of collaborative filtering computing method.Thus,this would require the academia to dig out more scientific,reasonable collaborative filtering recommendation algorithm.This paper relevant theories about recommendation algorithm.Analysis of the recommendation algorithm.This paper analyzes the advantages and disadvantages of content recommendation and collaborative filtering.On the basis of the theory,this paper introduces the proposal of improved recommendation algorithm and the extraction of content features,and briefly describes the improvement ideas and implementation steps of the improved recommendation algorithm.This paper introduces the content keyword extraction based on TF-IDF and the transformation from commodity evaluation matrix to content evaluation.This paper analyzes the recommendation algorithm based on collaborative filtering,proposes the improvement of collaborative filtering algorithm based on clustering analysis,describes the general steps of clustering analysis,clustering analysis based on k-means algorithm.Finally,this paper describes the process of improving the recommendation function experiment.This paper describes the process of improving the recommendation function experiment.Taking the book city recommendation on the net for example,carries on the concrete analysis.