Abstract
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 with the degree of interest. We let one reader as the target of mining and use classification analysis to discover the personalized book recommendations for the reader. In the mining process, we compute the degree of similarity of borrowing history records between the reader and other. If the degree conform the given condition, we assign the association level between the both readers is “high”. Otherwise, it is “low”. For books not borrowed by the reader, we treat those books as attributes for classification. First, we only consider readers ever borrowed books, and classify the borrowing history records to construct a decision tree. We can find the association level to be “high” between some attributes and the reader according to the decision tree. It is the basis to discover the most adaptive book recommendations for the reader. Moreover, we consider books with readers interests in the borrowing history records. Each book is divided to u unit items where u is the degree of the interest, u is positive integer, and the degrees of interest of these items are, respectively, from 1 to u. For books not borrowed by the reader, we divide those books to unit items and treat those items as attributes for classification. We can construct a decision tree after classifying the borrowing history records. According to the decision tree, we can find the association level to be “high” between some attributes and the reader. It is the basis to discover the most adaptive book recommendations for considering the reader’s interesting. The results of the mining can provide very useful information to recommend the most adaptive books for individual reader.
| Keywords: | data mining; classification analysis; borrowing history records; book recommendations |
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