Design And Implementation Of Personalized Library Recommendation System In University Library Based On Mahout

With the rapid development of network information technology,the service space of the university library is expanding,and the personalized service has gradually become the mainstream of the new service mode.For the vast number of users,it is very difficult to find the information they need in a large number of books in the university library.With the continuous development of the times,there are more and more new specialties,and the professional integration is stronger and stronger.The expansion of all kinds of professional knowledge makes university libraries have to expand their knowledge storage,which also provides a basic condition for the implementation of personalized recommendation system in libraries.Personalized library recommendation service changes the passive service mode of traditional library,and can recommend books to users actively according to the interest preference of users.The problem we need to solve in this paper is to track the latest library resources according to the different needs of users,inform users at the first time,achieve personalized service and optimize the utilization of resources,and turn the library service mode from passive to active.Collaborative filtering recommendation algorithm enables college teachers and students to find books and books that they need more accurately and quickly,reducing the pressure of Library Retrieval and saving a lot of time.The work of this article mainly includes the following points:(1)the optimization of the cold start problem,the improvement of the data sparsity problem and the processing of the missing data are proposed in this paper.(2)this paper discusses the technology involved,and analyzes the feasibility and quasi certainty,and discusses the recommendation algorithm and hybrid algorithm applied in personalized books.(3)this paper combines the actual demand of the recommended system and the research and implementation of the book recommendation system based on mahout.