Using Association Rules to Find Personalized Book Recommendation for Library

In this paper, we use readers’ borrowing history records as the source data of mining. Each borrowing history record contains a reader ever borrowed books and the reader’s the degrees of interest for those books. We let one reader as the target of mining, and use association rules to find the personalized book recommendations for the reader from two aspects, respectively. First, we only consider the books whether they are contained in borrowing history records or not, and propose a method to mine association rules whose antecedents are contained in the reader's borrowing history record. According to the characteristics of the association rules, we can find the adaptive book recommendations for the reader. Moreover, we consider the books with readers’ interests in the borrowing history records, and propose another method to mine association rules with interesting degrees whose antecedents are contained in the reader’s borrowing history record. According to the characteristics of the association rules with interesting degrees, we can find the reader’s the adaptive book recommendations for considering his interests. The results of the mining can provide very useful information to find personalized book recommendation for library.