Design and implementation of book publishing topic selection system based on collaborative filtering algorithm

Abstract

Abstract In view of the current book publishing topic planning may appear to rely on subjective experience of the problem. As well as book topic selection cannot be very good in line with the current hot vocabulary of this potential problem. Through the book sales market investigation, in line with the purpose of serving the book publishing industry. A collaborative filtering algorithm is used to design a business Intelligence Publishing topic selection system based on data mining. Follows are the process of processing data. First, we should put the crawl of online book information into the database. Second, filter to get the complete data we want. Finally, analysis the obtained data by collaborative filtering algorithm. This can be personalized to recommend the publishing house required keywords and the direction of the topic. Doing so can greatly reduce the burden on publishing house staff. It facilitates the development of the topic selection work of publishing house. Through the prediction of book publishing topic, we can understand the market law in a timely manner, cater to the customer’s consumption concept. In this way, we can greatly improve the efficiency of publishing house selection and reduce the consumption of human financial and material resources.