Open AccessDesign and implementation of book publishing topic selection system based on collaborative filtering algorithm
Author(s) -
Yuning Bian,
Yeli Li,
Qingtao Zeng,
Mengyang Li
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/563/5/052018
Subject(s) - publishing , selection (genetic algorithm) , computer science , filter (signal processing) , collaborative filtering , big data , electronic publishing , process (computing) , data publishing , data science , world wide web , information retrieval , recommender system , the internet , data mining , artificial intelligence , political science , law , computer vision , operating system
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.