A book recommendation algorithm based on collaborative filtering
Yuanqing Zhu · 2016
With the development of web technology and human society, people accumulate more and more data on the Internet. It contains numerous resources in this mass of data. How to explore the use of these resources has become an urgent problem. If you analyze these data sets, you can extract the hidden information or discover new knowledge. The method to obtain useful information from computer and techniques is called data mining. Searching engine is a common tool in today's information world. People use Google, Baidu, Bing and other search engines every day. These search service providers have been almost satisfied with the needs of most people. However, because of the universal nature, they can't give the accurate searching results according to users' different backgrounds and needs. Faced with this situation, it proposes the concept of personalized service, personalized recommendation system by establishing a binary relation between the user and information products, using the existing selection process or similar relationship, mining potential objects of interest to each user, and doing personalized recommendations. This article will do a research about the collaborative recommendation algorithm and propose a method about k-means clustering and k-nearest neighbor.